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Purchasing Department Email List

Human-Centered Systems are computer services that are implicitly, indirectly, and unobstructed to people whose primary goal it to interact

With other people. Computers are invisible – they act as electronic butlers. PD Email

It is a way to anticipate and meet people’s needs. Computers are thus introduced to a network of people interfacing with each other, instead of condemning.

Humans can operate in a loop computerized environment (see CHIL – Computers and the Human)

Interaction Loop [23]

Smart Room Environments are a new category of computer services. buy PD database for marketing

Computers monitor and interpret interactions and actions of people to help them communicate better. An example of an implementation is an automatic meeting support system. It tracks what was said, to whom and how.

It was [25]. Annotating speech recognition outputs with speakers’

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PD quality email lists

The meeting notes can be properly indexed and skimmed. They can also be searched and retrieved for identity, attention, and emotion. Sociallysupportive workspaces [23] and augmented multiparty interactions (26] encourage cooperation among participants in meetings, with multimodal interfaces to enter.

Facilitate functionalities and manipulate the contributions of participants

Monitor group activities. Other services that are offered within the framework

CHIL [23] includes better ways to connect people and support human memory. Computers must automatically collect context-aware information, such as meeting type, topic, and environmental, in order to provide these services.

conditions and participant characteristics, such as attentional state.

Speech Translation is an example of computer-mediated software that supports human-to-human communication [27,28 and 29]. Speech is a task.

Translation is the recognition of incoming speech in the source language. The translator output is translated into the target language text.

Synthesize the translated text into audible speech in target language. Most Purchasing Department Email List

Applications are designed in two-directional one-directional systems. Some systems may be configured as such.

Automatic language identification is used to route the speech into the appropriate system [30]. The translation should preserve the original meaning of the spoken input but also reflect other aspects.

These include politeness, respect and directness. These are just a few of the many aspects.

This could be directly derived by speaker characteristics such as generation

Synthesized output that is appropriate based on speaker’s gender or other relevant factors.

The identification of an individual’s emotional state in order to interpret and communicate emotions. Some aspects of the human body are also important.

Relationship between the speaker/listener In some languages, the word “word” is used.

The hierarchy between the sender and the receiver can affect the usage of the form. In this case, the wrong form could offend the receiver. Japanese is one example of such an.

52 T. Schultz

For example, Dr. Sadaoki Tomuko could be addressed as Tomukosan if he is

If the sender is not the boss, a friend or Tomukosensei should be contacted. This is how to address it

Problem solved, the English-Japanese JANUS Translation System [31] was created to address this problem.

You can switch between politeness levels.

2.3 Adaptation to System Components

The classification of speaker characteristics is a critical component, as we have already discussed.

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role in personalization and customization of applications. Speakers can also be used to customize and personalize applications.

To adapt components of the system to specific voice characteristics, it is necessary to assess the characteristics.

The content of the speech and the speaker. This adaptation has been completed.buy PD database for marketing

It has been shown to significantly improve recognition accuracy.

Compared favorably to overall system performance.

Traditional speech recognition adaptation is mostly concerned with the Purchasing Department Email List

The acoustic model and the language model can be adapted. The acoustic model was used in the early days.

An enrollment process that asked the user for permission to adapt was used.

Reading text prompts. This technique might prove to be very useful for power users.

The system allows you to store and preload speaker-specific acoustic models.

This enrollment process is tedious. You should therefore look for more recent information.

Systems rely on speaker adaptive learning methods which first determine the capabilities of the system. PD lists

The speaker’s identity is used to adapt the acoustic model based on that assumed identity. Some applications require a wider speaker class, such as gender.

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Pre-trained models to be loaded [32]. This is a dictionary.

Language model adaptation is the analysis of the topic or content of spoken input and then used to adapt [33]. Apart from speech recognition,

This technique can also be used to model other components of dialog. PD Email

Different dialog states or keywords that can trigger state switches.

Code switching is i.e. It is impossible to switch the language between utterances.

Monolingual speech recognition systems can handle this task. There have been efforts to

Multilingual speech recognition system is being developed [34]. But it seems favorable so far

To design language identification modules that direct speech input.

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to the appropriate monolingual recognition software [30]. Idiolect has demonstrated to

Accent has a significant impact on speaker recognition [35].

This has been shown to be detrimental to speech recognition performance. These characteristics have been classified with great care.

and the correct adaptation of system components. We refer to the following:

The reader is urged to [36].

2.4 Summary Purchasing Department Email List

This section concludes with a table that summarizes the speaker characteristics most relevant for human-computer and user-centered applications. It also includes references to studies or implementation examples.

thereof. This section does not cover all the applications mentioned.

They are also described in great detail elsewhere in this issue. These include

53 Speaker Characteristics

Forensic applications where the characteristics gender, age and medical conditions are known. buy PD database for marketing

Language, accent, and sociolect all play an important role. Jessen provides a comprehensive overview of forensic applications in this issue [37]. We did not also discuss

Emerging applications for home parole, detection and fraud in the

context of Law Enforcement that are concerned with the speaker’s identity

emotion. This article provides an introduction to the field regarding emotion.

Eriksson, in this issue [38].

Table 1. Table 1.

Reference to Characteristic Applications PD lists

Identity Transaction Authentication [39] ; Access Control [8]

Dialog Systems [14]; Meeting Browser [25]

Gender Dialog Systems [32]; Speech Synthesis ([3]

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age Dialog Systems [32]; Forensics [37]

Speech Synthesis [19]

health Forensics [37] PD email database

Language Call Routing [15] ; Speech Translation [30]

dialect Forensics [37]

Accent Language Learning [21]; Dialog Systems

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Speech Synthesis [19] ; Forensics[37]

Assessment Systems [20]

sociolect Forensics [37]

idiolect Speaker Recognition [35]; Forensics [37]

emotional state Translation [40]; Meeting browser [25] Purchasing Department Email List

Law Enforcement [38]; Dialog Systems (18,17]

Attentional state Human-Robot Interaction [41] Smart Workspaces (26,23,24]

Relationship/role Translation [31] PD database for sale

Cultural background Dialog Systems [22]

3 What? 3 What?

These are the discrete speaker classes to which vectors for speech features are assigned.

A speaker’s characteristics. These characteristics are relevant for speech-based applications. We have created a hierarchical structure.

As described above.

Figure 1 illustrates the proposal taxonomy. It distinguishes first and foremost between psychological and physiological aspects of speaker characteristics. The

These aspects are further sub-divisioned into those that concern each speaker

Contrary to those that are specific to a community or group. For

A speaker could be a professor at university, a wife or mother to her children, or both. The authority of a

The context in which the speaker speaks may be different. The hierarchy is determined by the people he/she talks to. Credibility may also depend on the person being done. Purchasing Department Email List

54 T. Schultz

Speaker Characteristics

Psychological

Individual collective identity, gender health, age PD database for sale

Geographic

Background

Attention

State

Emotional

State

Relationship role

language accent dialect

idiolect sociolect

Fig. 1. Taxonomy of Speaker Characteristics

business with, etc. The definition of the group “collective” is required.

A relationship between the sender and the receiver.

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This taxonomy has its limitations. It does not include all subjects. PD lists

Aspects of an individual (e.g. Weight, height, smoking and drinking habits, demographics like race, income mobility, employment status, or other special aspects

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Speech pathologies are an example of this, but instead we should be focusing on the characteristics that we think are important.

To be applicable (and assessable in the context typical speech applications. PD email database

The taxonomy also does not indicate which levels of linguistic information are required to distinguish between different characteristics. This is an example:

Low-level acoustic features can usually be used to distinguish gender; phonetic, however, is often sufficient.

To discriminate between idiolects, phonological and lexical knowledge may be necessary.

It needs syntactic and semantic information to distinguish sociolects.

To understand the role of speakers, and their role in society, pragmatics may be required. PD database for sale

Relationship to a group. Low-level physical aspects can be relatively simple

High level cues, which are hard to extract automatically, can be difficult to assess. This is a result.

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Most automatic systems for speaker recognition are still focused on low-level speakers.

cues. Purchasing Department Email List

The taxonomy does not include discrimination.

between stable versus transient characteristics. Speaker identity and gender are examples of stable characteristics. Transient characteristics can change over time

time. This aspect could be important for practical applications, particularly if it is a characteristic that underlies dynamic changes over the course of a single period.

audio recording session. Although there are locally stable characteristics like age and health,

Language, accent, dialect, and even idiolect can change much more slowly than the others.

Duration of the recording session, characteristics like attentional and emotional

55 Speaker Characteristics

The state of the speaker and the context or topic changing dynamically. The

Over the course of an interaction, the relationship between a speaker and the listener can change. The collective may also have other characteristics, such as sociolect.

The spoken language has many functions, including dialect, accent, and dialect.

It is quite stable within the same language. If a speaker changes languages during a recording session, then the class assignments for accent, idiolect and idiolect will be changed.

Both the dialect and English language can often change. PD address lists

3.1 Language-Dependent Speaker Characteristics

The following outlines the five characteristics language accent dialect.

Language-dependent speaker characteristics include sociolect and idiolect. They are somewhat dependent upon the language used by the speaker.

speaker.

The line between genuinely different languages and dialects of English is drawn

The same language can be the subject of many disputes. We define a dialect as a regional Purchasing Department Email List

Modifications at the lexical or grammatical levels are a variant of a language. Accent, on the other hand, is a regional variant that affects only pronunciation. It mainly affects phonetic realizations, but also prosody and allophonic distribution.

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Fluency and grammar. For example, British Received Pronunciation is an accent on English

Scottish English, on the other hand, would be considered a dialect because it frequently exhibits

Grammatical differences such as “Are you no going?” or “Aren’t you going?”

(see [42]). It is assumed that dialects of the same language can be understood by each other. PD address lists

Different languages may not be the same, but they are different. Languages need to be learned explicitly, even though they may not be the same.

Language speakers from other languages. Languages also have their own literary characteristics.

Tradition, dialects are primarily spoken varieties of languages without any literary tradition.

These definitions have been greatly simplified. Many languages do not have a written system, and therefore lack any literary tradition. The distinction between

Languages and dialects are a continuum, not a binary choice. They are often motivated by sociopolitical rather that linguistic considerations. Chinese

Languages, for instance, are unified by a common writing system. However they have their own distinct syntax. PD mailing lists

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a large number of unintelligible and mutually incompatible varieties, which differ significantly in grammar, vocabulary, pronunciation, and grammar. Most linguists will argue that PD email database

These variations can be considered different languages and are officially called dialects.

Encourage the idea of Chinese national unity (see “42”)). The exact opposite

This happened for Serbo­Croatian (the official language of the former Yugoslavia). After

After the split, the languages of Croatian and Serbian were referred to as

Separate languages to highlight national independence

Languages exhibit sociolectal and idiolectal variations in addition to regional variations.

variation. An idiolect is a speech pattern that has a consistent pronunciation, lexical choice or grammar and which is specific to one speaker. Some idiolectal patterns can include speaker-specific repetitive phrases (e.g. a tendency

To start sentences with Well, to put it simply …), characteristic intonation pattern,

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or divergent pronunciations (e.g. nucular instead of nuclear (see [42]). A sociolect is a collection of variations that are typical of a defined group of speakers.

Not by regional cohesion, but by social parameters like economic status and age PD address lists

56 T. Schultz

profession, etc. Some dialects can be considered both a sociolect and a dialect, as they often reflect a specific social status. You can take, for example:

Standard German is very similar to dialects spoken in Hannover or the Purchasing Department Email List

state of Saxony–Anhalt, which is the source of Martin Luther’s bible

Translation was the foundation for standard German development. Thus,

Standard German, while being a dialect in these specific areas, is also a sociolect in the sense that it carries a certain prestige as the national language.

Germany is used in all aspects of broadcasting, press and by citizens throughout the country.

higher education.

Despite all the efforts made to make speech recognition systems more robust for real-world applications, regional variations remain a major problem.

challenge. Non-native words can lead to significant increases in word error rates

[43,44] & dialectal speech [45]. This performance is a result of one thing.

Degradation is when acoustic models are customized and pronunciation dictionaries are targeted PD email database providers

towards native speakers and lacks the variety that comes from non-native pronunciations. The lexicon as well as the language model do not include dialectal variety. The

The straightforward solution to deploying accent- or dialect-specific speech recognizers is not possible due to two limitations: a lack of platform resources, and a lack of infrastructure.

Data. Mobile or automotive applications are particularly embedded and restrict the integration of multiple recognizers in one system. Even if

Resources permit the deployment dialect- or accent-specific systems. Purchasing Department Email List

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This results in very limited data resources. Real-world applications therefore require cross-dialect recognition or non-native recognition. Refer to the reader

For a complete introduction to this area, see [36]. You can find idiolectal features here. PD mailing lists

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This is used to tailor a speech application for a particular user, such as in training

A speech-based, automated office assistant. Additionally, the software includes idiolectal capabilities. PD email listing

[35] These features have been proven to be useful in automatic speaker identification. When developing an application, it is also possible to consider sociolectal characteristics.

an entire user group.

Individual

Accent

Phonetic Lexical

Grammar

k idiolect: l

Language

Collective

k sociolect l

Language k

Fig. 2. Language-dependent Characteristics PD email database providers

Speaker Characteristics 57

Multilingual environments may have an impact on idiolectal or sociolectal variations. For example, [46] has evidence that bilingual speakers alter their L1 speech after they speak in multilingual settings. Purchasing Department Email List

Spending time in a L2-speaking environment. There are many techniques that can improve your speech

Recognition performance in the presence code-switching has been studied [47,48]. The act of using words and phrases from code-switching is known as code-switching.

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Multilingual speakers often use different languages in the same sentence.

Engaged in informal conversations

Figure 2 shows the similarities and differences between the languagedependent characteristics language dialect accent, sociolect, idiolect and accent. Main

The effects of linguistic aspects on discriminating factors and whether they are relevant to the individual, are considered discriminating factors.

Individuals and groups can have the same characteristics.

4 How? 4 How?

Characteristics

The most prominent and widely studied task in investigating the is probably the.

“Assignment speech features to discrete speakers classes” is speaker recognition

(Who is speaking, class=identity) & language identification (which language).

spoken, class=language). Speech recognition (what is being said, class=content) addresses buy PD database online

This is a larger problem that could be considered part of the “Speaker Classification”

When high-level characteristics such as topic, content, or role are being investigated. As it has become apparent that there are solutions to these three tasks, they have grown closer. Purchasing Department Email List

One task might be more beneficial than the other and all three must be done together.

Study of speech-based real-world applications to improve speech quality The following are the results.

We will briefly discuss speaker recognition and language identification. This section

It is not intended to be a complete introduction. For more information, the reader should refer to

Refer to in-depth overviews such as [49] language identification and [39.50] for language classification.

For speaker recognition. This article is a good introduction to speech recognition. PD mailing lists

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4.1 Speaker Recognition

The level of linguistic understanding applied to solving the classification task can help distinguish between different classification approaches. Reynolds identifies a hierarchy perceptual cues humans use to recognize speakers. PD email listing

[39]. At the highest level, people use semantics and diction.

These are ideosynchrasies that arise from socio-economic status and education.

the place of birth of the speaker. Prosodic features are located on the second level.

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The characteristics of personality and parental influence are rhythm, speed and intonation. People are at the lowest level of linguistic communication.

Use the acoustic aspect of sounds such as nasality and breathiness to determine their authenticity.

Let’s draw some conclusions about the anatomical structure and vocal range of the speaker.

apparatus. Low level physical cues can be extracted automatically. However, it is difficult to determine high-level cues. Most automatic systems are therefore able to detect high-level cues.

Systems for speaker recognition still focus on low-level cues.

58 T. Schultz

Conventional systems use Gaussian Mixture Models to capture Purchasing Department Email List

frame-level characteristics [52]. GMMs are often unable to distinguish speaker-specific information that changes over multiple frames, as speech frames are presumed to be independent of each other. GMMs do not perform well. buy PD database online

These are able to discriminate speakers based upon higher-level differences such as idiolect. GMMs can also be affected by mismatched acoustics

Conditions that rely solely on low-level speech signal features. To overcome

These problems have led to speaker recognition focusing on higher-level linguistic features such as phonetic information emerging out of speaker ideosynchrasies [35]. This is known as phonetic speaker recognition. It applies relative

Frequency from phone ngrams [53]. This method is being intensively researched [39] and further developed by various modeling strategies, variations in statistical

n-gram models [54], various classifiers such as Support Vector Machines [55],

Modeling cross-stream dimensions to uncover underlying phone dependencies across multiple language [54,56].

4.2 Language Identification

Language identification methods can be classified in the same way as speaker recognition. This classification is based on the level of linguistic data.

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task. [49] distinguishes the signal processing level, and the unit level (e.g. phones),

The sentence level is the word level. These levels allow him to distinguish between spectral features-based acoustic approaches and language identification.

derived from speech segments [57], Phonotactic approaches that use the contraints relative frequencies of sound unit [58], and other derivatives

Multilingual phone recognizers used as tokenizers [59], extended N-grams [60],

Cross-stream modeling [61], as well as combinations of GMMs, phonotactic and phonotactic model [62]. Navr’atil [49] also lists prosodic approaches that use tone

Intuition, prominence, and prominence [63], as well as those approaches that use full speech buy PD database online

Recognition of language identification [64]

5 A Classification System For Speaker Characteristics

This section presents a general classification system that applies one framework to the classification and classification of different speaker characteristics. Purchasing Department Email List

Identity, gender, accent, proficiency, level of attention, and language

speaker. Framework uses high-level phonetic information in order to capture speakers’ ideasynchrasies. This was originally proposed by [58] within the context of language.

Identification and [35] in context of speaker recognition. The idea behind the basic concept is to

To decode speech using various phone recognizers, and to use the relative frequency of

Phone n-grams are features for training speaker characteristics models and their

classification. By using more language-independent languages, we enrich existing algorithms with the application of different speaker characteristics.

Phone recognizers and modeling dependencies across multiple phone channels PD quality email lists

[54]. We also investigate the impact of different decision rules and examine their implications.

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Speaker Characteristics 59

Consider the number of languages involved and compare multilingual to multi-engineering. PD email listing

Approaches with respect to classification performance

5.1 Multilingual Phone Sequences

Our experiments were done using the GlobalPhone phone recognition software

Project [65] is available in 12 languages Arabic (AR), Mandarin Chinese(CH),

Croatian (KR), German, French (DE), Japanese (JA), Korean [KO], Portuguese (PO), Russian (“RU”), Spanish (SP), Swedish “SW”) and Turkish (“TU”)

These phone recognizers were trained with the Janus Speech Recognition PD email id list

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Toolkit. The acoustic model is composed of a context-independent, 3-state HMM

16 Gaussians per State in the system. The 13 Mel-scale is the basis of the Gaussians

Cepstral power and coefficients, with first- and second-order derivatives. These are the following Purchasing Department Email List

Cepstral subtraction, Linear Discriminant Analysis reduces the input vector

Up to 16 dimensions. Vocal tract length normalization (VTLN), is a part of training.

Normalization of speaker sounds To decode, unsupervised MLLR is used to locate the

Best matching warp factor to the test speaker. The decoding process is done with

Viterbi searches using a fully connected network of monophones null-grammar networks

i.e. The recognition process does not require any prior knowledge of phone statistics.

Figure 3 illustrates the relationship between phone unit count and phone error.

Rates for ten languages

A language dependent phonetic model (n-gram) is created to train a model for a specific speaker characteristic. It is based on available training data.

We train phonetic bigram models based on the CMUCambridge Statistical language model toolkit [19]. Phonetic bigram models can be directly estimated using the data and not by applying universal background models, or adapting with background models. No transcriptions PD email id list

Speech data is required for any stage of model training. Figure 4 illustrates the

Procedure for training a speaker identity model speaker k.

Phone recognizers (PR1 ,…,PRm), decode speaker k’s training data to create m phone strings. These phone strings are used to create m phonetic bigram models.

(PM1,k ,…,PMm) are estimates for speaker k.

needs to be classified as one of the following: n-class speaker characteristic, m phone

Recognition software will produce models of m x 10 phonetic bigrams.

Each of the PRi m-phone recognizers, which are used to train phonetic bigram models, decodes the audio segment. Each of the resulting M phone strings is scored against one of the n bigram model PMi,j. This Purchasing Department Email List

This results in a perplexity matrix PP. The PPij element of the phonetic bigram model PMi on the phone string output is phone

Recognizer PRi We will investigate other options in future experiments. PD email id list

Our default decision algorithm is C* to suggest a class estimate

Select the j

Lowest

i(PP)i,j . This procedure is illustrated in Figure 5. We refer to it as MPM-pp.

The MPM-pp classification method is used in the following.

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PD business email database free download

There are many classification tasks that can be done in relation to speaker characteristics.

Language classification

This section applies the MPM-pp framework for the multiclassification problem in four languages: Japanese, Russian (RU), Spanish and Spanish (SP).

Turkish (TU) We used a limited number of phone recognitions in PD quality email lists

PD business database

Languages other than the four languages of classification are used to duplicate the

Common circumstances for our identification experiments and to show a PD email leads

Language independence that is maintained even when the language is identified

65 Speaker Characteristics

Table 8. Confusion matrix for 3-way proficiency classification using 6 versus 7 phone

Recognizers

Phonetic 6 languages 7 langues

model C-1 C-2 C-3 C-1 C-2 C-3

C-1 8 3 19 8 5 17

C-2 8 41 61 6 53 51

C-3 2 12 99 1 20 92

domain. Phone recognizers in Chinese, German, and French

With phone vocabulary sizes of 145 to 47 and 42 respectively, these were borrowed from

The GlobalPhone project. Data for this experiment were also available.

It was borrowed from GlobalPhone but not used for training phone recognizers. It was broken up as shown in Table 9 Training was done with data set 1.

The phonetic models were used, and data set 4 was not available during training. PD email id list

It is used to assess the performance of the entire classifier. Data

Sets 2 and 3 were used to experiment with different materials.

Decision strategies

Table 9. Table 9.

Set JA RU SP TU

nspk 1-20 20 20 20/20 20

2 5 10 9 10

3 3 5 5 5

4 3 5 4 5

nutt all 2294 4923 2724 2924 Purchasing Department Email List

tutt all 6 hrs 9 hrs 8 hrs 7 hrs

To train the phonetic bigram models, you will need to utterances starting from set 1 in each Lj

Each of the three phone recognition algorithms PRi was used to decode JA, RO, SP, and TU

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PD email database free download

CH, DE, FR. Twelve trigram models with Kneser/Ney were created

No cut-off or backoff. The size of the training corpora varied from 140K to 500,000.

250K tokens. All 12 models had trigram coverage of 73% to 95%.

Unigram coverage less than 1%

Our lowest average perplexity decision was the first to benchmark accuracy.

rule. We constructed a 4-class multi-classifier using the same principles.

Data set 2 was for each of the durations tk: 5s, 10s and 20s respectively; data set 3 was

Cross-validation

Multi-classifier combines the outputs of multiple binary classifiers. buy PD email database

ECOC is error-correcting output coding. A class space that contains 4 languages produces 7 binary partitions. Each of these was taught an independent instructor.

Multilayer Perceptron (MLP), 12 input units, 1 output unit that uses scaled

Conjugate gradients for data set 2 and early stopping with cross-validation

Crossover

Passive crossover networks are built using capacitors, inductors, or both.

Resistors are used to split the audio signal for high-frequency information

goes to the low-frequency information and high-frequency driver

The woofer is the winner. Passive crossovers offer the best value for money

Allow the amplifier to power the loudspeaker PD quality email lists

channel. Passive crossovers work in most cases but are not recommended. Purchasing Department Email List

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They are less precise than active crossovers.

To operate, active crossovers need external power. They can be powered by external power.

The output of a mixer or preamplifier is connected to the output of an amplifier. PD email leads

Power amplifier. Active crossover has the advantage of being able to control power amplifiers.

You can change many characteristics, such as the crossover frequency.

The rate of transition.

A active crossover is usually an additional component you will need

To purchase. Check that your loudspeaker system is certified.

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PD b2b database

Separate inputs are needed to accommodate multiple amplifiers.

Your crossover. An especially cost-effective method to obtain an

Active crossover can be achieved by purchasing a Digital Signal Processor, (DSP).

You can create equalizers and crossovers. buy PD email database

Woofer

Bosch Security Systems 9 (c) 2012 Bosch Security Systems Purchasing Department Email List

The signal is sent through a loudspeaker…or more.

The signal chain contains the audio signal.

Use an instrument or microphone to communicate your message.

The Signal Chain

A sound system is a system that transmits an audio signal from its source to the system components. These components balance, process and amplify the signal.

A loudspeaker, or headphones, is released. The signal chain is the audio signal’s path.

Some signal chains can be complex with many components and divergences. Others are simple. Below is a diagram of a signal chain.

Basic, stripped-down signal chains

What Does an Audio System Look Like?

(c) 2012 Bosch Security Systems 10.

What Signal Voltage is and why it matters

Signal voltage refers to the value of the signal being sent to an audio device. We usually refer to the value in decibels volts (dBV).

Each microphone, amplifier, pickup, and source of program has a unique voltage level. However, the signals cannot be mixed until they are all combined. Purchasing Department Email List

They are all promoted to “line” professional status. buy PD email database

Mic level is the relatively

Low-level signal (generally)

From -40 to -60 dBV (of a)

Microphone that must be

Amplified to line-level,

It is easier to manipulate purchase PD email lists

A mixing console.

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Different levels of signals

Enter a mixer, but make sure the signal is clear

Professionals can take your leaves. PD email leads

line level. The standard is

Audio: +4 dBu audio or -10 dBV

Levels, or about 1V.

We need stronger signals

to drive loudspeakers.

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PD b2c database

Amplifiers boost processed

Signals to the speaker level

So the loudspeakers are rewarded

There is enough power to get the job done. Purchasing Department Email List

(c) 2012 Bosch Security Systems

11

Coverage Pattern is a directional pattern for a loudspeaker system that can be varied by frequency or tone. One example is a loudspeaker’s

One output may cover a large and narrow area of a room while another might cover an equally long and tall section.

Pro Tip:

Make sure your loudspeakers cover your entire audience. People who are unable to hear you clearly are more likely not to be able to hear you.

Throw things on the stage. email marketing database PD

Attachment: A cabinet that houses loudspeaker drivers as well as associated electronic hardware such crossover circuits. An

An enclosure can be as simple as a wooden box, or it could be complex and contain ports, baffles and acoustic insulation. The enclosure

This prevents sound waves from interfering between the rear and front, reduces vibration and heat generation.

Driver coils. It also shapes the low frequency response. To do this, the enclosure design must be compatible with the driver characteristics.

This was achieved with great success. Electro-Voice was the first company to implement the concept of matching enclosure designs systematically.

Drivers should be able to extend the low end as much as possible. Purchasing Department Email List

Frequency Response is a measurement of how well a speaker or electronic component reproduces sounds. Frequency: A frequency

Response specification refers to the frequency range and deviation from a perfectly flat response.

Frequency response curves can be used to show the accuracy of sound systems.

Frequency Range is the lower and higher limits of the system’s output. It is not possible for a loudspeaker to reproduce the sound of another speaker.

Anything below or over its rated frequency range is unacceptable, Buddy.

Pro Tip: email marketing database PD

Different speaker manufacturers may use different standards. To make an accurate comparison, be sure to read the manufacturer’s footnotes.

The frequency range of a frequency measured at the -10 dB points will usually be larger than that measured at the 3 dB points. However, it is not fair.

Considerations when choosing a loudspeaker

Here are some helpful terms

You’ll be able to explore your options for loudspeakers and you’ll see many fancy terms such as frequency, impedance, and frequency.

Response, power rating, SPL. They are, however, very important. Here’s a quick primer.

Bosch Security Systems 12 (c) 2012 Bosch Security Systems

Impedance (Z), The resistance that an electronic circuit or device offers to the AC current flowing through it

It. It is often represented by the mathematical symbol Z and measured in Ohms. Impedance

This article describes the difficulty of a speaker to drive and its compatibility for various amplifiers. purchase PD email lists

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Pro Tip:

Before you purchase your loudspeakers, make sure to check the compatibility of your amplifiers. We are here to help.

Power Rating: Peak and continuous power ratings can be measured separately. Continuous indicates how much. PD email Profile

The power that the loudspeaker can handle over time. Peak shows you how much power the loudspeaker can handle in short bursts.

PD customers database
PD customers database

In other words, you shouldn’t expect to melt your faces at the peak rating for very long. Many manufacturers do also.

A “life test” is used to assess how well a loudspeaker will withstand abuse. Every loudspeaker is subject to a 100-hour testing at EV.

Loudspeaker used to confirm the continuous power handling measurement.

Sound Pressure Level (SPL), The volume of an acoustic sound expressed in decibels (dB). Maximum SPL

This is the maximum sound pressure level that a loudspeaker can tolerate before distortion occurs. Purchasing Department Email List

A Few (or More) Helpful Terms

(c) 2012 Bosch Security Systems 13

It’s time for the professionals to help you when you’re in charge of a show like this.

What number of loudspeakers do I need?

One sound system is the most basic. email marketing database PD

loudspeaker. That’s fine for some people. If the venue is

Cozy (for example, when you are playing a didgeridoo show at a coffee shop).

Shop), one loudspeaker might be all you need.

The louder the speakers, the larger the venue.

need.

This policy consists of allowing the update operation of the tuple and in
perform compensating operations that set null values to attributes
of the foreign key of the tuples that refer to it; this action is carried out
to maintain referential integrity.
Since relational DBMS generally allow establishing that a
certain attribute of a relation does not allow null values, it can only be
apply the override policy if the foreign key attributes do ad-
miten. CPO mailing lists
Override application example
The best way to understand what annulment is is through an example. We have
the following relationships:
• SELLERS relationship:
• CUSTOMER relationship:
* {vendedorasig} reference SELLERS.
More specifically, cancellation in case of deletion consists of per-
allow the deletion of a tuple t that has a referenced key and, in addition,
else, modify all tuples that reference t, so that the
attributes of the corresponding foreign key take null values.
Similarly, cancellation in case of modification consists of
allow modification of attributes of the primary key of a tuple

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buy CPO email database


t that has a referenced key and additionally modify all tuples
referencing t, so that the attributes of the corresponding foreign key
slope take null values.
 FUOC • 71Z799014MO 30 The relational model and relational algebra
a) If we apply the annulment in case of deletion and, for example, we want to delete the seller
number 1, all the clients that had it assigned will be modified, and they will have a value
null lor in sellersig. We will have:
• SELLERS relationship:
• CUSTOMER relationship:
* {vendedorasig} reference SELLERS.
b) If we apply the override on modification, and now we want to change the number
of the seller 2 by 5, all the clients that had it assigned will be modified and will pass to
have a null value in sellersig. We will have:
• SELLERS relationship:
• CUSTOMER relationship:
* {vendedorasig} reference SELLERS.
4.3.4. Selection of the maintenance policy
of referential integrity email marketing database CPO
We have seen that in case of deletion or modification of a primary key,
differentiated by some foreign key, there are several key maintenance policies.
the referential integrity rule.

CPO email Profile

The way to define these policies of
maintaining integrity with
the SQL language is explained in the unit
“The SQL language” of this course.
 FUOC • 71Z799014MO 31 The relational model and relational algebra CPO address lists
The designer can choose for each foreign key which policy will be applied in
case of deletion of the referenced primary key, and which in case of modification
tion of it. The designer must take into account the meaning of each key
concrete foreign to be able to choose appropriately.
4.4. Domain Integrity Rule
The domain integrity rule is related, as its name suggests,
with the notion of domain. This rule establishes two conditions.

email marketing database CPO
email marketing database CPO


This condition implies that all non-null values contained in the base of
data for a given attribute must be from the domain declared for di-
cho attribute.
Example
If in the relation EMPLOYEES (DNI, name, surname, ageemp) we have declared that domi-
nio(DNI) is the predefined domain of integers, so we will not be able to insert, for example,
For example, no employee whose DNI has the value “Luis”, which is not an integer.
Let us remember that domains can be of two types: predefined or defined.
two per user. Note that user-defined domains result in
so very useful, because they allow us to determine more specifically
what will be the values admitted by the attributes.
Example
Suppose now that in the relation EMPLOYEES(DNI, name, surname, ageemp) we have
declared that domain(empage) is the domain defined by the user age. suppose
also that the age domain has been defined as the set of integers between
16 and 65. In this case, for example, it will not be possible to insert an employee with a value of 90
for ageemp.
The second condition of the domain integrity rule is more complex,
especially in the case of user-defined domains; the DBMS ac-
Current ones do not support it for these last domains. For these reasons only the
We will present superficially.
The first condition is that a non-null value of an attribute
A i must belong to the domain of the attribute A i; that is, it must belong
to domain(Ai).
This second condition serves to establish that the operators that

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_CPO consumer email database


can be applied to the values depend on the domains of these
values; that is, a given operator can only be applied on
values that have domains that are suitable for it.
Policy Enforcement CPO mailing lists
different
It may happen that for a
certain foreign key, the
appropriate policy in case
erase is different from the
suitable in case of modification
tion. For example, it can be
need to apply the restriction
in case of deletion and update
cascading in case
of modification.
Remember that the concepts
predefined domain and domain
user-defined have been explained
in subsection 2.2 of this unit
didactic
further reading
To study in more detail
the second condition
of the integrity rule
of domain, you can consult
the following work:
CJ Date (2001).
Introduction to systems
of databases (7th ed.,
chap. 19). Prentice Hall.
 FUOC • 71Z799014MO 32 The relational model and relational algebra
Example
We will analyze this second condition of the domain integrity rule with an example
concrete. If in the relation EMPLOYEES (DNI, name, surname, ageemp) it has been declared that
domain(DNI) is the predefined domain of integers, so it will not be allowed to query
all those employees whose DNI is equal to ‘Elena’ (DNI = ‘Elena’). The reason is not
it makes sense that the comparison operator = be applied between a DNI that has for domain
nio the integers, and the value ‘Elena’, which is a character string.
Thus, the fact that the operators that can be applied to the email marketing database CPO
values depend on the domain of these values allows to detect errors that are
they might commit when the database is queried or updated. The domi-
User-defined definitions are very useful, because they will allow us to determine
specify more specifically which operators can be applied
about values.

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Example
Let’s look at another example with user-defined domains. Suppose that in the knowledge
gives relationship EMPLOYEES (DNI, name, surname, ageemp) it has been declared that domain (DNI)
is the domain defined by the user IDnumbers and that domain(ageemp) is the domain of-
user-defined age. Suppose that DNInumbers corresponds to the positive integers CPO database for sale
and what age corresponds to the integers that are between 16 and 65. In this case, it will be incorrect,
for example, query the employees who have the DNI value equal to the empage value.
The reason is that, although both the DNI and empage values are integers, their do-
minions are different; therefore, according to the meaning that the user gives them, it does not make sense
compare them.

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CPO email database free


However, current relational DBMSs do not support the second con-
addition of the domain integrity rule for domains defined by the
Username. If you wanted to do it, it would be necessary for the designer to have some
way of specifying, for each operator that one wanted to use, for what
User-defined domain joins make sense to apply.
The SQL standard language does not currently include this possibility.
 FUOC • 71Z799014MO 33 The relational model and relational algebra
5. The relational algebra
As we have already commented in the section dedicated to the operations of the
relational, relational algebra is inspired by set theory to
specify queries in a relational database.
To specify a query in relational algebra, you must define one or more
more steps that serve to build, through algebra operations
relational, a new relation that contains the data that responds to the relation
results from the stored relationships. Languages based on the alge-
relational code are procedural, since the steps that make up the query
describe a procedure.
The vision that we will present is that of a theoretical language and, therefore, we will include-
We only describe its fundamental operations, and not the constructions that could be
add to a commercial language to facilitate issues such as the or-
presentation of the result, the calculation of aggregate data, etc.
Relational algebra operations have been classified according to different criteria. CPO quality email lists
theria; of all of them we indicate the following three:
1) Depending on whether or not they can be expressed in terms of other operations.
a) Primitive operations: are those operations from which we can
Let’s define the rest. These operations are union, difference, product.
Cartesian to, selection and projection.
b) Non-primitive operations: the rest of the operations of the relational algebra
that are not strictly necessary, because they can be expressed in terms of
we of the primitives; however, non-primitive operations allow

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formulate some queries more comfortably. There are different versions
relational algebra, depending on the non-primitive operations included. Nope-
We will study the non-primitive operations that are used most frequently.
sequence: the intersection and the combination.
2) According to the relationship number ones that have as operands:
a) Binary operations: they are those that have two relations as operands.
All operations except selection and projection are binary.
A remarkable feature of all the operations of relative algebra
The rationale is that both the operands and the result are relations. Is
property is called a relational closure.
Consult section 3
of this teaching unit.
Closure Implications
relational
The fact that the result
of an algebra operation
relational be a new
relationship has implications
important:
1. The result of an operation
tion can act as
operand of another operation.
2. The result of a email marketing database CPO
operation will meet all
the features that already
We know about the relationships:
non-ordering of tuples,
absence of repeated tuples,
etc.

CPO customers database

 FUOC • 71Z799014MO 34 The relational model and relational algebra
b) Unary operations: they are those that have a single relation as operand.
do. Selection and projection are unary.
3) According to whether or not they resemble the operations of set theory:
a) Set operations: they are those that resemble those of the theory of CPO database for sale
sets. These are the union, intersection, difference, and product.
Cartesian.
b) Specifically relational operations: they are the rest of the operations;
that is, selection, projection, and combination.
As we have already mentioned, the relational algebra operations
result in a new relationship.

 

That is, if we do an operation
tion of algebra such as EMPLOYEES_ADM ∪ ∪ EMPLOYEES_PROD
to obtain the union of the relations EMPLOYEES_ADM and EMPLOYEES_PROD,
the result of the operation is a new relation that has the union of the tuples
of the starting relationships.
This new relationship must have a name. In principle, we consider that your
name is the same relational algebra expression that gets it; namely,
the same expression EMPLOYEES_ADM ∪ EMPLOYEES_PROD. Since this CPO quality email lists
name is long, sometimes it can be interesting to change it for one more
simple. This will make it easier for us to refer to the new relationship, and it will be especially
mind useful in cases where we want to use it as an operand of another
operation. We will use the helper operation rename for this purpose.
In the example, to give the name EMPLOYEES to the relation resulting from the
operation EMPLOYEES_ADM ∪ EMPLOYEES_PROD, we would do:
EMPLOYEES := EMPLOYEES_ADM ∪ EMPLOYEES_PROD.
Each relational algebra operation gives default names to the attributes.
Butos of the scheme of the resulting relationship, as we will see later.
In some cases, it may be necessary to change these default names to
other names. For this reason, we will also allow renaming
the relation and its attributes using the rename operation.
The rename operation, which we will denote with the symbol :=, allows
assign a name R to the relation that results from an operation of the
relational algebra; it does it as follows:
R := E,
where E is the expression of a relational algebra operation.
algebra operations
relational classified according to
whether they are ensemble or specifically
relationships are studied in
subsections 5.1 and 5.2 of this unit.
 FUOC • 71Z799014MO 35 The relational model and relational algebra
Here is an example that we will use to illustrate the
relational algebra operations. Later we will see in detail the operations
tions.
Suppose we have a relational database with the four relations
following tions:
1) The relationship BUILDINGS_EMP, which contains data from different buildings of the
that a company has to carry out its activities.
2) The DESPACHOS relation, which contains data on each of the dispatches
that is in the previous buildings.
3) The relation EMPLOYEES_ADM, which contains the data of the employees of
the company that carry out administrative tasks.
4) The relation EMPLOYEES_PROD, which stores the data of the employees CPO consumer email database
of the company dealing with production tasks.
Next we describe the schemes of the previous relations and their ex-
stresses at a given time:

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Cartesian product
If we calculate the Cartesian product of BUILDINGS_EMP and OFFICES, we will obtain a
new relation containing all possible concatenations of EMP_BUILDINGS tuples
with DISPATCH tuples.
If you want to calculate the Cartesian product of two relations that have some CPO database for sale
common attribute name, it is only necessary to previously rename the attributes
suitable butos of one of the two relationships.
Next we define the attributes and the extension of the resulting relation
of a Cartesian product.
Cartesian product example
The Cartesian product of the relations OFFICES and BUILDINGS_EMP in the example can be
do as indicated (it is necessary to rename attributes previously):
BUILDINGS(buildingname, supmediadesp) := BUILDINGS_EMP(building, supmediadesp).
R := BUILDINGS × OFFICES.
Then, the resulting relation R will be:
The Cartesian product is an operation that, from two relations,
obtains a new relation formed by all the tuples that result
of concatenating tuples from the first relation with tuples from the second.
The Cartesian product is a binary operation. Being T and S two rela-
tions that satisfy that their schemas do not have any name of
common attribute, the Cartesian product of T and S is denoted as T × S.
The schema attributes of the resulting relation of T × S are all
the attributes of T and all the attributes of S*.
The extension of the resulting relation of T × S is the set of all
the tuples of the form <v1, v2, …, v n , w1, w2, …, w m> for which
ple that <v1, v2, …, v n> belongs to the extension of T and that <w1, w2, …,
w m> belongs to the extension of S.
R
buildingname supmediadesp building number area
Navy 15 Navy 120 10 CPO quality email lists
Navy 15 Navy 230 20
Marine 15 Diagonal 120 10
Marina 15 Diagonal 440 10
Diagonal 10 Navy 120 10
* Remember that T and S do not have
no common attribute name.
 FUOC • 71Z799014MO 41 The relational model and relational algebra
It should be noted that the Cartesian product is an operation that is rarely
is used explicitly, because the result it gives is usually not useful for
resolve common queries.
Despite this, the Cartesian product is included in the relational algebra because
which is a primitive operation; from which another operation of the
algebra, the combination, which is used very often.
5.2. Specifically relational operations
The specifically relational operations are selection, projection
and the combination.
5.2.1. Selection
To obtain a relationship that has all the offices of the Marina building that have more
of 12 square meters, we can apply a selection to the DESPACHOS relationship with a
selection condition that is building = Marina and area > 12; DESPA- would be indicated
CHOS(building = Marina and area > 12).
In general, the selection condition C is made up of one or more clauses
from the way:

buildingname supmediadesp building number area
Diagonal 10 Marina 230 20
Diagonal 10 Diagonal 120 10
Diagonal 10 Diagonal 440 10
We can see the selection as an operation that serves to choose some
some tuples from a relation and remove the rest. More specifically, the
selection is an operation that, from a relation, obtains a CPO consumer email database
new relation formed by all the tuples of the starting relation
that meet a specified selection condition.

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Selection is a unary operation. Being C a condition of se-
lesson, the selection of T with the condition C is denoted as T(C).
 FUOC • 71Z799014MO 42 The relational model and relational algebra
where Ai and Aj are attributes of the relation T, θ is a comparison operator*
and v is a value. Furthermore, it holds that: buy CPO database for marketing
• In clauses of the form Ai θ v, v is a value of the domain of Ai.
• In clauses of the form Ai, θ Aj , Ai and Aj have the same domain.
The clauses that form a selection condition are connected with the following:
following boolean operators: “and” (∧) and “or” (∨).
Next we define the attributes and the extension of the resulting relation
of a selection.
Selection Example
If we want to obtain a relation R with the dispatches of the database of the example that
are in the Marina building and that have an area of more than 12 square meters,
We will make the following selection:
R := OFFICES(building = Marina and area > 12).
The resulting relation R will be:
5.2.2. Projection
The schema attributes of the resulting relation of T(C) match
with the attributes of the schema of the relation T.
The extension of the resulting relation of T(C) is the set of tuples
that belong to the extension of T and that satisfy the condition of se-
lesson C. A tuple t satisfies a selection condition C if, after
substituting each attribute in C for its value in t, the condition C
evaluates to the true value.
Rbuilding number surface
Navy 230 20
We can consider the projection as an operation that serves to
choose some attributes of a relation and eliminate the rest. More concrete-
Simply put, projection is an operation that, starting from a relation,
obtains a new relation formed by all the (sub)tuples of the relation purchase CPO email lists
starting relationship that result from removing specified attributes.
* That is, =, ≠, <, ≤, >, or ≥.
 FUOC • 71Z799014MO 43 The relational model and relational algebra
To obtain a relationship that has only the first and last name attributes of the employees of
administration, we can make a projection in the relation EMPLOYEES_ADM on these
two attributes. It would be indicated as follows: EMPLOYEES_ADM [name, surname].
Next we will define the attributes and the extension of the resulting relationship.
much of a projection.
projection example
If we want to obtain a relation R with the name and surname of all the employees of
administration of the example database, we will make the following projection:
R := EMPLOYEES_ADM[first name, last name].
Then, the resulting relation R will be:
5.2.3. Combination
The projection is a unary operation. Being {Ai , Aj, …, A k} a subcon-
along with the attributes of the schema of the relation T, the projection of T
over {A i, Aj , …, Ak} is denoted as T[Ai , A j, …, Ak].
The schema attributes of the resulting relation of T[Ai, Aj, …, Ak]are the attributes {Ai, Aj, …, Ak}.
The extension of the relation resulting from T[Ai , Aj , …, Ak] is the set
to of all tuples of the form <t.Ai, t.A j, …, t.A k>, where it holds
that t is a tuple of the extension of T and where t.Ap denotes the value for
the Ap attribute of tuple t.
R
name last Name
John Garcia
Martha Rock
The combination is an operation that, starting from two relations, obtains
ne a new relation formed by all the tuples that result from con-
string tuples from the first relation with tuples from the second, and that
satisfy a specified join condition.
The join is a binary operation. Being T and S two relations CPO consumer email database
whose schemas have no common attribute name, and being
B a combination condition, the combination of T and S according to the con-
addition B is indicated T[B]S.
Elimination of tuples

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Note that the projection
implicitly remove all
the repeated tuples. The result-
do of a projection is a
valid relationship and cannot buy CPO database for marketing
have repetitions of tuples.
 FUOC • 71Z799014MO 44 The relational model and relational algebra
To get a relationship that has the data of each one of the administrative employees,
together with the data of the offices where they work, we can make a comparison purchase CPO email lists
combination of the relations EMPLOYEES_ADM and OFFICES, where the condition of
combination indicate the following: buildingdesp = building and numberdesp = number. The condition
The merge function causes the result to only merge the data of one employee with
the data of an office if the office building and the office number of the employee are equal to
building and office number, respectively. That is, the condition makes the
an employee’s data is combined with the data of the office where she works, but not
with data from other offices.
The combination of the previous example would be indicated as follows:
EMPLOYEES_ADM[buildingdesp = building, numberdesp = number] OFFICES.
If you want to combine two relations that have some attribute name co-
common, it is only necessary to previously rename the repeated attributes of a
Of the two.
In general, the condition B of a combination T[B]S is formed by one or
more comparisons of the form
Ai θ Aj ,
where A i is an attribute of the relation T, Aj is an attribute of the relation S, θ is
a comparison operator ( =, ≠, <, ≤, >, ≥), and it is true that Ai and Aj have
the same domain. Comparisons of a join condition are
separated by commas.
Next we define the attributes and the extension of the resulting relation
of a combination.
Combination Example
Let us suppose that we want to find the data of the offices that have a ma-
greater than or equal to the average area of the offices in the building where they are located. The IF-
The following combination will provide us with the data of these dispatches together with the data of
your building (note that the attributes must first be renamed):
BUILDINGS(buildingname,srmediasp) := BUILDINGS_EMP(building,srmediasp),
The schema attributes of the resulting relation of T[B]S are all
two the attributes of T and all the attributes of S*.
The extension of the resulting relation of T[B]S is the set of tu-
plas that belong to the extension of the Cartesian product T × S and that
satisfy all the comparisons that form the combination condition. purchase CPO email lists
nation B. A tuple t satisfies a comparison if, after substituting
each attribute that appears in the comparison by its value in t, the comparison
ration is evaluated to the true value.
* Remember that T and S do not have
no common attribute name.
 FUOC • 71Z799014MO 45 The relational model and relational algebra
R := BUILDINGS[buildingname = building, supmediadesp ≤ surface] OFFICES.
Then, the resulting relation R will be:
Suppose now that in order to obtain the data of each one of the administrative employees,
tion, together with the data of the office where they work, we use the following combination:
R := EMPLOYEES_ADM[buildingdesp = building, numberdesp = number] OFFICES.
The resulting relation R will be:
The relation R combines the data of each employee with the data of her office.
The combination is sometimes called a θ-combination, and when
all comparisons of the join condition have the operator
“=”, is called an equijoin.
According to this, the combination of the last example is an equijoin.
Note that the result of an equijoin always includes one or CPO email database free
more pairs of attributes that have identical values in all tuples.
In the example above, the values of buildingoff match those of building, and the values of
despnumber match those of number.
Since one of each pair of attributes is superfluous, a
combination variant called natural combination, in order to
remove them.

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Example of natural combination
If we do:
R := BUILDINGS_EMP * OFFICES,
R
buildingname supmediadesp building number area
Navy 15 Navy 230 20
Diagonal 10 Diagonal 120 10
Diagonal 10 Diagonal 440 10
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DNI name surname desp building desp number building surface area number
40,444,255 Juan Garcia Navy 120 Navy 120 10
33,567,711 Marta Roca Navy 120 Navy 120 10
The natural combination of two relations T and S is denoted as T * S
and basically consists of equijoining followed by elimination.
tion of superfluous attributes; In addition, it is considered by default that
the join condition matches all pairs of attributes that
they have the same name in T and in S.
Note that, unlike equicombination, natural combination
tural applies to relationships that have common attribute names.
 FUOC • 71Z799014MO 46 The relational model and relational algebra
the condition is considered to be building = building because building is the only attribute name
buto that appears both in the EDIFICIOS_EMP scheme and in the OFFICES scheme.
The result of this natural combination is:
Notice that one of the building name attributes has been removed.
Sometimes, before the natural combination it is necessary to apply the operation
rename function to match the names of the attributes that we in-
teresa match buy CPO targeted email list
Example of natural combination with redenomination
For example, if we want to obtain the data of each of the administration employees
together with the data of the office where they work but without repeating values of super-
fluos, we will make the following natural combination, which requires a previous redenomination:

DNI name surname building desp number desp area
40,444,255 Juan Garcia Marina 120 10
33,567,711 Marta Roca Marina 120 10
In many cases, to formulate a query in relational algebra it is
Several operations must be used, which are applied in a certain order.
To do so, there are two possibilities:
1) Use a single algebra expression that includes all the operations
tions with the necessary parentheses to indicate the order of application.
2) Decompose the expression into several steps where each step applies
a single operation and obtain an intermediate relationship that can be used
perform in the subsequent steps.
 FUOC • 71Z799014MO 47 The relational model and relational algebra
Example of using sequences of operations
To obtain the name and surname of the employees, both administrative and pro-
duction, it is necessary to make a union of EMPLOYEES_ADM and EMPLOYEES_PROD, and then
then make a projection on the first and last name attributes. The operation can be
express in the following ways: CPO email database free
a) A single expression can be used:
R := (EMPLOYEES_ADM ∪ EMPLOYEES_PROD) [first name, last name].
b) Or we can express it in two steps:
• EMPS := EMPLOYEES_ADM ∪ EMPLOYEES_PROD;
• R := EMPS[first name, last name]

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In cases where a query requires many operations to be performed, the result
The second alternative is simpler, because it avoids complex expressions.
Other examples of queries formulated with sequences of operations
Let’s look at some examples of database queries formulated with sequences of operations.
relational algebra rations. buy CPO targeted email list
1) To get the building name and house number offices located in buildings in
where the average area of these offices is greater than 12, we can use the following
sequence of operations:
• A := BUILDINGS_EMP(supmediaoff > 12);
• B := OFFICES * A;
• R := B[building, number]2) Suppose now that you want to get the first and last name of all employees
(both administration and production) who are assigned to office 120 of the building
Marine job. In this case, we can use the following sequence:
• A := EMPLOYEES_ADM ∪ EMPLOYEES_PROD;
• B := A(buildingdesp = Marina and numberdesp = 120);
• R := B[first name, last name].
3) If we want to consult the name of the building and the number of the offices that no
admin employee is assigned, we can use this sequence:
• A := OFFICES [building, number];
• B := EMPLOYEES_ADM[despbuilding,despnumber];
• R := A – B.
4) To obtain the DNI, the name and surname of all the administration employees who
have office, together with the surface of your office, we can do the following:
• A[DNI, name, surname, building, number] := EMPLOYEES_ADM[DNI, name, surname, building,
officedesp, numberdesp];
• B := A * OFFICES;
• R := B[DNI, name, surname, area].
5.4. Extensions: outer joins
To finish the subject of relational algebra, we will analyze some extensions
useful combination.
The combinations that have been described obtain the tuples of the Cartesian-
not of two relations that satisfy a join condition.
It should be noted that tuples that have a null value for any of the attributes
butts contained in the join condition are always lost, because in
In these cases the join condition always evaluates to false.
In some cases, it may be interesting to make combinations of the data from two re-
relationships without loss of data from the starting relationships. Then,
outer joins are used. buy CPO targeted email list
R
DNI emp empname emp surname desp building desp number area
33,567,711 Marta Roca Marina 120 10
55,898,425 Carlos Buendia Diagonal 120 10
77,232,144 Elena Pla Marina 230 20
Outer joins between two relations T and S consist of va-
Combination variants that preserve all tuples in the result
of T, of S or of both relations. They can be of the following types:
1) The left outer join between two relations T and S, which
we denote as T[C]IS, keeps in the result all the tuples of the
T relationship.
2) The right outer join between two relations T and S, which
we denote as T[C] DS, keeps in the result all the tuples of
the s relationship.
3) Finally, the full outer join between two relations T
and S, which we denote as T[C]pS, preserves in the result all tu-
plas of T and all tuples of S.
The combinations have
explained in subsection 5.3.3
of this teaching unit.
 FUOC • 71Z799014MO 49 The relational model and relational algebra
These extensions also apply to the case of the natural combination between
two relations, T * S, namely:
a) The left outer natural join between two relations T and S, which
denoted as T *I S, preserves all tuples of relation T in the result.
b) The right outer natural join between two relations T and S, which is
denoted as T *D S, keeps in the result all the tuples of the relation S.
c) Finally, the full outer natural join between two relations T and S,
denoted as T *P S , preserves in the result all tuples of T and all
the tuples of S.
The tuples of a relation T that are preserved in the result R of a combination
external nation with another relation S, even though they do not satisfy the condition
combination, have null values in the R result for all attributes
which come from the relation S.CPO email database free

This policy consists of allowing the update operation of the tuple and in
perform compensating operations that set null values to attributes
of the foreign key of the tuples that refer to it; this action is carried out
to maintain referential integrity.
Since relational DBMS generally allow establishing that a
certain attribute of a relation does not allow null values, it can only be
apply the override policy if the foreign key attributes do ad-
miten. CPO mailing lists
Override application example
The best way to understand what annulment is is through an example. We have
the following relationships:
• SELLERS relationship:
• CUSTOMER relationship:
* {vendedorasig} reference SELLERS.
More specifically, cancellation in case of deletion consists of per-
allow the deletion of a tuple t that has a referenced key and, in addition,
else, modify all tuples that reference t, so that the
attributes of the corresponding foreign key take null values.
Similarly, cancellation in case of modification consists of
allow modification of attributes of the primary key of a tuple

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t that has a referenced key and additionally modify all tuples
referencing t, so that the attributes of the corresponding foreign key
slope take null values.
 FUOC • 71Z799014MO 30 The relational model and relational algebra
a) If we apply the annulment in case of deletion and, for example, we want to delete the seller
number 1, all the clients that had it assigned will be modified, and they will have a value
null lor in sellersig. We will have:
• SELLERS relationship:
• CUSTOMER relationship:
* {vendedorasig} reference SELLERS.
b) If we apply the override on modification, and now we want to change the number
of the seller 2 by 5, all the clients that had it assigned will be modified and will pass to
have a null value in sellersig. We will have:
• SELLERS relationship:
• CUSTOMER relationship:
* {vendedorasig} reference SELLERS.
4.3.4. Selection of the maintenance policy
of referential integrity email marketing database CPO
We have seen that in case of deletion or modification of a primary key,
differentiated by some foreign key, there are several key maintenance policies.
the referential integrity rule.

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The way to define these policies of
maintaining integrity with
the SQL language is explained in the unit
“The SQL language” of this course.
 FUOC • 71Z799014MO 31 The relational model and relational algebra CPO address lists
The designer can choose for each foreign key which policy will be applied in
case of deletion of the referenced primary key, and which in case of modification
tion of it. The designer must take into account the meaning of each key
concrete foreign to be able to choose appropriately.
4.4. Domain Integrity Rule
The domain integrity rule is related, as its name suggests,
with the notion of domain. This rule establishes two conditions.

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email marketing database CPO


This condition implies that all non-null values contained in the base of
data for a given attribute must be from the domain declared for di-
cho attribute.
Example
If in the relation EMPLOYEES (DNI, name, surname, ageemp) we have declared that domi-
nio(DNI) is the predefined domain of integers, so we will not be able to insert, for example,
For example, no employee whose DNI has the value “Luis”, which is not an integer.
Let us remember that domains can be of two types: predefined or defined.
two per user. Note that user-defined domains result in
so very useful, because they allow us to determine more specifically
what will be the values admitted by the attributes.
Example
Suppose now that in the relation EMPLOYEES(DNI, name, surname, ageemp) we have
declared that domain(empage) is the domain defined by the user age. suppose
also that the age domain has been defined as the set of integers between
16 and 65. In this case, for example, it will not be possible to insert an employee with a value of 90
for ageemp.
The second condition of the domain integrity rule is more complex,
especially in the case of user-defined domains; the DBMS ac-
Current ones do not support it for these last domains. For these reasons only the
We will present superficially.
The first condition is that a non-null value of an attribute
A i must belong to the domain of the attribute A i; that is, it must belong
to domain(Ai).
This second condition serves to establish that the operators that

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can be applied to the values depend on the domains of these
values; that is, a given operator can only be applied on
values that have domains that are suitable for it.
Policy Enforcement CPO mailing lists
different
It may happen that for a
certain foreign key, the
appropriate policy in case
erase is different from the
suitable in case of modification
tion. For example, it can be
need to apply the restriction
in case of deletion and update
cascading in case
of modification.
Remember that the concepts
predefined domain and domain
user-defined have been explained
in subsection 2.2 of this unit
didactic
further reading
To study in more detail
the second condition
of the integrity rule
of domain, you can consult
the following work:
CJ Date (2001).
Introduction to systems
of databases (7th ed.,
chap. 19). Prentice Hall.
 FUOC • 71Z799014MO 32 The relational model and relational algebra
Example
We will analyze this second condition of the domain integrity rule with an example
concrete. If in the relation EMPLOYEES (DNI, name, surname, ageemp) it has been declared that
domain(DNI) is the predefined domain of integers, so it will not be allowed to query
all those employees whose DNI is equal to ‘Elena’ (DNI = ‘Elena’). The reason is not
it makes sense that the comparison operator = be applied between a DNI that has for domain
nio the integers, and the value ‘Elena’, which is a character string.
Thus, the fact that the operators that can be applied to the email marketing database CPO
values depend on the domain of these values allows to detect errors that are
they might commit when the database is queried or updated. The domi-
User-defined definitions are very useful, because they will allow us to determine
specify more specifically which operators can be applied
about values.

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Example
Let’s look at another example with user-defined domains. Suppose that in the knowledge
gives relationship EMPLOYEES (DNI, name, surname, ageemp) it has been declared that domain (DNI)
is the domain defined by the user IDnumbers and that domain(ageemp) is the domain of-
user-defined age. Suppose that DNInumbers corresponds to the positive integers CPO database for sale
and what age corresponds to the integers that are between 16 and 65. In this case, it will be incorrect,
for example, query the employees who have the DNI value equal to the empage value.
The reason is that, although both the DNI and empage values are integers, their do-
minions are different; therefore, according to the meaning that the user gives them, it does not make sense
compare them.

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However, current relational DBMSs do not support the second con-
addition of the domain integrity rule for domains defined by the
Username. If you wanted to do it, it would be necessary for the designer to have some
way of specifying, for each operator that one wanted to use, for what
User-defined domain joins make sense to apply.
The SQL standard language does not currently include this possibility.
 FUOC • 71Z799014MO 33 The relational model and relational algebra
5. The relational algebra
As we have already commented in the section dedicated to the operations of the
relational, relational algebra is inspired by set theory to
specify queries in a relational database.
To specify a query in relational algebra, you must define one or more
more steps that serve to build, through algebra operations
relational, a new relation that contains the data that responds to the relation
results from the stored relationships. Languages based on the alge-
relational code are procedural, since the steps that make up the query
describe a procedure.
The vision that we will present is that of a theoretical language and, therefore, we will include-
We only describe its fundamental operations, and not the constructions that could be
add to a commercial language to facilitate issues such as the or-
presentation of the result, the calculation of aggregate data, etc.
Relational algebra operations have been classified according to different criteria. CPO quality email lists
theria; of all of them we indicate the following three:
1) Depending on whether or not they can be expressed in terms of other operations.
a) Primitive operations: are those operations from which we can
Let’s define the rest. These operations are union, difference, product.
Cartesian to, selection and projection.
b) Non-primitive operations: the rest of the operations of the relational algebra
that are not strictly necessary, because they can be expressed in terms of
we of the primitives; however, non-primitive operations allow

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formulate some queries more comfortably. There are different versions
relational algebra, depending on the non-primitive operations included. Nope-
We will study the non-primitive operations that are used most frequently.
sequence: the intersection and the combination.
2) According to the relationship number ones that have as operands:
a) Binary operations: they are those that have two relations as operands.
All operations except selection and projection are binary.
A remarkable feature of all the operations of relative algebra
The rationale is that both the operands and the result are relations. Is
property is called a relational closure.
Consult section 3
of this teaching unit.
Closure Implications
relational
The fact that the result
of an algebra operation
relational be a new
relationship has implications
important:
1. The result of an operation
tion can act as
operand of another operation.
2. The result of a email marketing database CPO
operation will meet all
the features that already
We know about the relationships:
non-ordering of tuples,
absence of repeated tuples,
etc.

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 FUOC • 71Z799014MO 34 The relational model and relational algebra
b) Unary operations: they are those that have a single relation as operand.
do. Selection and projection are unary.
3) According to whether or not they resemble the operations of set theory:
a) Set operations: they are those that resemble those of the theory of CPO database for sale
sets. These are the union, intersection, difference, and product.
Cartesian.
b) Specifically relational operations: they are the rest of the operations;
that is, selection, projection, and combination.
As we have already mentioned, the relational algebra operations
result in a new relationship.

 

That is, if we do an operation
tion of algebra such as EMPLOYEES_ADM ∪ ∪ EMPLOYEES_PROD
to obtain the union of the relations EMPLOYEES_ADM and EMPLOYEES_PROD,
the result of the operation is a new relation that has the union of the tuples
of the starting relationships.
This new relationship must have a name. In principle, we consider that your
name is the same relational algebra expression that gets it; namely,
the same expression EMPLOYEES_ADM ∪ EMPLOYEES_PROD. Since this CPO quality email lists
name is long, sometimes it can be interesting to change it for one more
simple. This will make it easier for us to refer to the new relationship, and it will be especially
mind useful in cases where we want to use it as an operand of another
operation. We will use the helper operation rename for this purpose.
In the example, to give the name EMPLOYEES to the relation resulting from the
operation EMPLOYEES_ADM ∪ EMPLOYEES_PROD, we would do:
EMPLOYEES := EMPLOYEES_ADM ∪ EMPLOYEES_PROD.
Each relational algebra operation gives default names to the attributes.
Butos of the scheme of the resulting relationship, as we will see later.
In some cases, it may be necessary to change these default names to
other names. For this reason, we will also allow renaming
the relation and its attributes using the rename operation.
The rename operation, which we will denote with the symbol :=, allows
assign a name R to the relation that results from an operation of the
relational algebra; it does it as follows:
R := E,
where E is the expression of a relational algebra operation.
algebra operations
relational classified according to
whether they are ensemble or specifically
relationships are studied in
subsections 5.1 and 5.2 of this unit.
 FUOC • 71Z799014MO 35 The relational model and relational algebra
Here is an example that we will use to illustrate the
relational algebra operations. Later we will see in detail the operations
tions.
Suppose we have a relational database with the four relations
following tions:
1) The relationship BUILDINGS_EMP, which contains data from different buildings of the
that a company has to carry out its activities.
2) The DESPACHOS relation, which contains data on each of the dispatches
that is in the previous buildings.
3) The relation EMPLOYEES_ADM, which contains the data of the employees of
the company that carry out administrative tasks.
4) The relation EMPLOYEES_PROD, which stores the data of the employees CPO consumer email database
of the company dealing with production tasks.
Next we describe the schemes of the previous relations and their ex-
stresses at a given time:

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Cartesian product
If we calculate the Cartesian product of BUILDINGS_EMP and OFFICES, we will obtain a
new relation containing all possible concatenations of EMP_BUILDINGS tuples
with DISPATCH tuples.
If you want to calculate the Cartesian product of two relations that have some CPO database for sale
common attribute name, it is only necessary to previously rename the attributes
suitable butos of one of the two relationships.
Next we define the attributes and the extension of the resulting relation
of a Cartesian product.
Cartesian product example
The Cartesian product of the relations OFFICES and BUILDINGS_EMP in the example can be
do as indicated (it is necessary to rename attributes previously):
BUILDINGS(buildingname, supmediadesp) := BUILDINGS_EMP(building, supmediadesp).
R := BUILDINGS × OFFICES.
Then, the resulting relation R will be:
The Cartesian product is an operation that, from two relations,
obtains a new relation formed by all the tuples that result
of concatenating tuples from the first relation with tuples from the second.
The Cartesian product is a binary operation. Being T and S two rela-
tions that satisfy that their schemas do not have any name of
common attribute, the Cartesian product of T and S is denoted as T × S.
The schema attributes of the resulting relation of T × S are all
the attributes of T and all the attributes of S*.
The extension of the resulting relation of T × S is the set of all
the tuples of the form <v1, v2, …, v n , w1, w2, …, w m> for which
ple that <v1, v2, …, v n> belongs to the extension of T and that <w1, w2, …,
w m> belongs to the extension of S.
R
buildingname supmediadesp building number area
Navy 15 Navy 120 10 CPO quality email lists
Navy 15 Navy 230 20
Marine 15 Diagonal 120 10
Marina 15 Diagonal 440 10
Diagonal 10 Navy 120 10
* Remember that T and S do not have
no common attribute name.
 FUOC • 71Z799014MO 41 The relational model and relational algebra
It should be noted that the Cartesian product is an operation that is rarely
is used explicitly, because the result it gives is usually not useful for
resolve common queries.
Despite this, the Cartesian product is included in the relational algebra because
which is a primitive operation; from which another operation of the
algebra, the combination, which is used very often.
5.2. Specifically relational operations
The specifically relational operations are selection, projection
and the combination.
5.2.1. Selection
To obtain a relationship that has all the offices of the Marina building that have more
of 12 square meters, we can apply a selection to the DESPACHOS relationship with a
selection condition that is building = Marina and area > 12; DESPA- would be indicated
CHOS(building = Marina and area > 12).
In general, the selection condition C is made up of one or more clauses
from the way:

buildingname supmediadesp building number area
Diagonal 10 Marina 230 20
Diagonal 10 Diagonal 120 10
Diagonal 10 Diagonal 440 10
We can see the selection as an operation that serves to choose some
some tuples from a relation and remove the rest. More specifically, the
selection is an operation that, from a relation, obtains a CPO consumer email database
new relation formed by all the tuples of the starting relation
that meet a specified selection condition.

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Selection is a unary operation. Being C a condition of se-
lesson, the selection of T with the condition C is denoted as T(C).
 FUOC • 71Z799014MO 42 The relational model and relational algebra
where Ai and Aj are attributes of the relation T, θ is a comparison operator*
and v is a value. Furthermore, it holds that: buy CPO database for marketing
• In clauses of the form Ai θ v, v is a value of the domain of Ai.
• In clauses of the form Ai, θ Aj , Ai and Aj have the same domain.
The clauses that form a selection condition are connected with the following:
following boolean operators: “and” (∧) and “or” (∨).
Next we define the attributes and the extension of the resulting relation
of a selection.
Selection Example
If we want to obtain a relation R with the dispatches of the database of the example that
are in the Marina building and that have an area of more than 12 square meters,
We will make the following selection:
R := OFFICES(building = Marina and area > 12).
The resulting relation R will be:
5.2.2. Projection
The schema attributes of the resulting relation of T(C) match
with the attributes of the schema of the relation T.
The extension of the resulting relation of T(C) is the set of tuples
that belong to the extension of T and that satisfy the condition of se-
lesson C. A tuple t satisfies a selection condition C if, after
substituting each attribute in C for its value in t, the condition C
evaluates to the true value.
Rbuilding number surface
Navy 230 20
We can consider the projection as an operation that serves to
choose some attributes of a relation and eliminate the rest. More concrete-
Simply put, projection is an operation that, starting from a relation,
obtains a new relation formed by all the (sub)tuples of the relation purchase CPO email lists
starting relationship that result from removing specified attributes.
* That is, =, ≠, <, ≤, >, or ≥.
 FUOC • 71Z799014MO 43 The relational model and relational algebra
To obtain a relationship that has only the first and last name attributes of the employees of
administration, we can make a projection in the relation EMPLOYEES_ADM on these
two attributes. It would be indicated as follows: EMPLOYEES_ADM [name, surname].
Next we will define the attributes and the extension of the resulting relationship.
much of a projection.
projection example
If we want to obtain a relation R with the name and surname of all the employees of
administration of the example database, we will make the following projection:
R := EMPLOYEES_ADM[first name, last name].
Then, the resulting relation R will be:
5.2.3. Combination
The projection is a unary operation. Being {Ai , Aj, …, A k} a subcon-
along with the attributes of the schema of the relation T, the projection of T
over {A i, Aj , …, Ak} is denoted as T[Ai , A j, …, Ak].
The schema attributes of the resulting relation of T[Ai, Aj, …, Ak]are the attributes {Ai, Aj, …, Ak}.
The extension of the relation resulting from T[Ai , Aj , …, Ak] is the set
to of all tuples of the form <t.Ai, t.A j, …, t.A k>, where it holds
that t is a tuple of the extension of T and where t.Ap denotes the value for
the Ap attribute of tuple t.
R
name last Name
John Garcia
Martha Rock
The combination is an operation that, starting from two relations, obtains
ne a new relation formed by all the tuples that result from con-
string tuples from the first relation with tuples from the second, and that
satisfy a specified join condition.
The join is a binary operation. Being T and S two relations CPO consumer email database
whose schemas have no common attribute name, and being
B a combination condition, the combination of T and S according to the con-
addition B is indicated T[B]S.
Elimination of tuples

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Note that the projection
implicitly remove all
the repeated tuples. The result-
do of a projection is a
valid relationship and cannot buy CPO database for marketing
have repetitions of tuples.
 FUOC • 71Z799014MO 44 The relational model and relational algebra
To get a relationship that has the data of each one of the administrative employees,
together with the data of the offices where they work, we can make a comparison purchase CPO email lists
combination of the relations EMPLOYEES_ADM and OFFICES, where the condition of
combination indicate the following: buildingdesp = building and numberdesp = number. The condition
The merge function causes the result to only merge the data of one employee with
the data of an office if the office building and the office number of the employee are equal to
building and office number, respectively. That is, the condition makes the
an employee’s data is combined with the data of the office where she works, but not
with data from other offices.
The combination of the previous example would be indicated as follows:
EMPLOYEES_ADM[buildingdesp = building, numberdesp = number] OFFICES.
If you want to combine two relations that have some attribute name co-
common, it is only necessary to previously rename the repeated attributes of a
Of the two.
In general, the condition B of a combination T[B]S is formed by one or
more comparisons of the form
Ai θ Aj ,
where A i is an attribute of the relation T, Aj is an attribute of the relation S, θ is
a comparison operator ( =, ≠, <, ≤, >, ≥), and it is true that Ai and Aj have
the same domain. Comparisons of a join condition are
separated by commas.
Next we define the attributes and the extension of the resulting relation
of a combination.
Combination Example
Let us suppose that we want to find the data of the offices that have a ma-
greater than or equal to the average area of the offices in the building where they are located. The IF-
The following combination will provide us with the data of these dispatches together with the data of
your building (note that the attributes must first be renamed):
BUILDINGS(buildingname,srmediasp) := BUILDINGS_EMP(building,srmediasp),
The schema attributes of the resulting relation of T[B]S are all
two the attributes of T and all the attributes of S*.
The extension of the resulting relation of T[B]S is the set of tu-
plas that belong to the extension of the Cartesian product T × S and that
satisfy all the comparisons that form the combination condition. purchase CPO email lists
nation B. A tuple t satisfies a comparison if, after substituting
each attribute that appears in the comparison by its value in t, the comparison
ration is evaluated to the true value.
* Remember that T and S do not have
no common attribute name.
 FUOC • 71Z799014MO 45 The relational model and relational algebra
R := BUILDINGS[buildingname = building, supmediadesp ≤ surface] OFFICES.
Then, the resulting relation R will be:
Suppose now that in order to obtain the data of each one of the administrative employees,
tion, together with the data of the office where they work, we use the following combination:
R := EMPLOYEES_ADM[buildingdesp = building, numberdesp = number] OFFICES.
The resulting relation R will be:
The relation R combines the data of each employee with the data of her office.
The combination is sometimes called a θ-combination, and when
all comparisons of the join condition have the operator
“=”, is called an equijoin.
According to this, the combination of the last example is an equijoin.
Note that the result of an equijoin always includes one or CPO email database free
more pairs of attributes that have identical values in all tuples.
In the example above, the values of buildingoff match those of building, and the values of
despnumber match those of number.
Since one of each pair of attributes is superfluous, a
combination variant called natural combination, in order to
remove them.

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Example of natural combination
If we do:
R := BUILDINGS_EMP * OFFICES,
R
buildingname supmediadesp building number area
Navy 15 Navy 230 20
Diagonal 10 Diagonal 120 10
Diagonal 10 Diagonal 440 10
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DNI name surname desp building desp number building surface area number
40,444,255 Juan Garcia Navy 120 Navy 120 10
33,567,711 Marta Roca Navy 120 Navy 120 10
The natural combination of two relations T and S is denoted as T * S
and basically consists of equijoining followed by elimination.
tion of superfluous attributes; In addition, it is considered by default that
the join condition matches all pairs of attributes that
they have the same name in T and in S.
Note that, unlike equicombination, natural combination
tural applies to relationships that have common attribute names.
 FUOC • 71Z799014MO 46 The relational model and relational algebra
the condition is considered to be building = building because building is the only attribute name
buto that appears both in the EDIFICIOS_EMP scheme and in the OFFICES scheme.
The result of this natural combination is:
Notice that one of the building name attributes has been removed.
Sometimes, before the natural combination it is necessary to apply the operation
rename function to match the names of the attributes that we in-
teresa match buy CPO targeted email list
Example of natural combination with redenomination
For example, if we want to obtain the data of each of the administration employees
together with the data of the office where they work but without repeating values of super-
fluos, we will make the following natural combination, which requires a previous redenomination:

DNI name surname building desp number desp area
40,444,255 Juan Garcia Marina 120 10
33,567,711 Marta Roca Marina 120 10
In many cases, to formulate a query in relational algebra it is
Several operations must be used, which are applied in a certain order.
To do so, there are two possibilities:
1) Use a single algebra expression that includes all the operations
tions with the necessary parentheses to indicate the order of application.
2) Decompose the expression into several steps where each step applies
a single operation and obtain an intermediate relationship that can be used
perform in the subsequent steps.
 FUOC • 71Z799014MO 47 The relational model and relational algebra
Example of using sequences of operations
To obtain the name and surname of the employees, both administrative and pro-
duction, it is necessary to make a union of EMPLOYEES_ADM and EMPLOYEES_PROD, and then
then make a projection on the first and last name attributes. The operation can be
express in the following ways: CPO email database free
a) A single expression can be used:
R := (EMPLOYEES_ADM ∪ EMPLOYEES_PROD) [first name, last name].
b) Or we can express it in two steps:
• EMPS := EMPLOYEES_ADM ∪ EMPLOYEES_PROD;
• R := EMPS[first name, last name]

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In cases where a query requires many operations to be performed, the result
The second alternative is simpler, because it avoids complex expressions.
Other examples of queries formulated with sequences of operations
Let’s look at some examples of database queries formulated with sequences of operations.
relational algebra rations. buy CPO targeted email list
1) To get the building name and house number offices located in buildings in
where the average area of these offices is greater than 12, we can use the following
sequence of operations:
• A := BUILDINGS_EMP(supmediaoff > 12);
• B := OFFICES * A;
• R := B[building, number]2) Suppose now that you want to get the first and last name of all employees
(both administration and production) who are assigned to office 120 of the building
Marine job. In this case, we can use the following sequence:
• A := EMPLOYEES_ADM ∪ EMPLOYEES_PROD;
• B := A(buildingdesp = Marina and numberdesp = 120);
• R := B[first name, last name].
3) If we want to consult the name of the building and the number of the offices that no
admin employee is assigned, we can use this sequence:
• A := OFFICES [building, number];
• B := EMPLOYEES_ADM[despbuilding,despnumber];
• R := A – B.
4) To obtain the DNI, the name and surname of all the administration employees who
have office, together with the surface of your office, we can do the following:
• A[DNI, name, surname, building, number] := EMPLOYEES_ADM[DNI, name, surname, building,
officedesp, numberdesp];
• B := A * OFFICES;
• R := B[DNI, name, surname, area].
5.4. Extensions: outer joins
To finish the subject of relational algebra, we will analyze some extensions
useful combination.
The combinations that have been described obtain the tuples of the Cartesian-
not of two relations that satisfy a join condition.
It should be noted that tuples that have a null value for any of the attributes
butts contained in the join condition are always lost, because in
In these cases the join condition always evaluates to false.
In some cases, it may be interesting to make combinations of the data from two re-
relationships without loss of data from the starting relationships. Then,
outer joins are used. buy CPO targeted email list
R
DNI emp empname emp surname desp building desp number area
33,567,711 Marta Roca Marina 120 10
55,898,425 Carlos Buendia Diagonal 120 10
77,232,144 Elena Pla Marina 230 20
Outer joins between two relations T and S consist of va-
Combination variants that preserve all tuples in the result
of T, of S or of both relations. They can be of the following types:
1) The left outer join between two relations T and S, which
we denote as T[C]IS, keeps in the result all the tuples of the
T relationship.
2) The right outer join between two relations T and S, which
we denote as T[C] DS, keeps in the result all the tuples of
the s relationship.
3) Finally, the full outer join between two relations T
and S, which we denote as T[C]pS, preserves in the result all tu-
plas of T and all tuples of S.
The combinations have
explained in subsection 5.3.3
of this teaching unit.
 FUOC • 71Z799014MO 49 The relational model and relational algebra
These extensions also apply to the case of the natural combination between
two relations, T * S, namely:
a) The left outer natural join between two relations T and S, which
denoted as T *I S, preserves all tuples of relation T in the result.
b) The right outer natural join between two relations T and S, which is
denoted as T *D S, keeps in the result all the tuples of the relation S.
c) Finally, the full outer natural join between two relations T and S,
denoted as T *P S , preserves in the result all tuples of T and all
the tuples of S.
The tuples of a relation T that are preserved in the result R of a combination
external nation with another relation S, even though they do not satisfy the condition
combination, have null values in the R result for all attributes
which come from the relation S.CPO email database free

Human-Centered Systems are computer services that are implicitly, indirectly, and unobstructed to people whose primary goal it to interact

With other people. Computers are invisible – they act as electronic butlers. email marketing database PD

It is a way to anticipate and meet people’s needs. Computers are thus introduced to a network of people interfacing with each other, instead of condemning.

Humans can operate in a loop computerized environment (see CHIL – Computers and the Human)

Interaction Loop [23] Purchasing Department Email List

Smart Room Environments are a new category of computer services.

Computers monitor and interpret interactions and actions of people to help them communicate better. An example of an implementation is an automatic meeting support system. It tracks what was said, to whom and how.

It was [25]. Annotating speech recognition outputs with speakers’ PD email Profile

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The meeting notes can be properly indexed and skimmed. They can also be searched and retrieved for identity, attention, and emotion. Sociallysupportive workspaces [23] and augmented multiparty interactions (26] encourage cooperation among participants in meetings, with multimodal interfaces to enter. purchase PD email lists

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Facilitate functionalities and manipulate the contributions of participants

Monitor group activities. Other services that are offered within the framework PD email Profile

CHIL [23] includes better ways to connect people and support human memory. Computers must automatically collect context-aware information, such as meeting type, topic, and environmental, in order to provide these services.

conditions and participant characteristics, such as attentional state.

Speech Translation is an example of computer-mediated software that supports human-to-human communication [27,28 and 29]. Speech is a task.

Translation is the recognition of incoming speech in the source language. The translator output is translated into the target language text.

Synthesize the translated text into audible speech in target language. Most email marketing database PD

Applications are designed in two-directional one-directional systems. Some systems may be configured as such. Purchasing Department Email List

Automatic language identification is used to route the speech into the appropriate system [30]. The translation should preserve the original meaning of the spoken input but also reflect other aspects.

These include politeness, respect and directness. These are just a few of the many aspects.

This could be directly derived by speaker characteristics such as generation

Synthesized output that is appropriate based on speaker’s gender or other relevant factors.

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The identification of an individual’s emotional state in order to interpret and communicate emotions. Some aspects of the human body are also important.

Relationship between the speaker/listener In some languages, the word “word” is used.

The hierarchy between the sender and the receiver can affect the usage of the form. In this case, the wrong form could offend the receiver. Japanese is one example of such an.

52 T. Schultz

For example, Dr. Sadaoki Tomuko could be addressed as Tomukosan if he is

If the sender is not the boss, a friend or Tomukosensei should be contacted. This is how to address it email marketing database PD

Problem solved, the English-Japanese JANUS Translation System [31] was created to address this problem. Purchasing Department Email List

You can switch between politeness levels.

2.3 Adaptation to System Components

The classification of speaker characteristics is a critical component, as we have already discussed.

role in personalization and customization of applications. Speakers can also be used to customize and personalize applications.

To adapt components of the system to specific voice characteristics, it is necessary to assess the characteristics.

The content of the speech and the speaker. This adaptation has been completed.

It has been shown to significantly improve recognition accuracy.

Compared favorably to overall system performance.

Traditional speech recognition adaptation is mostly concerned with the

The acoustic model and the language model can be adapted. The acoustic model was used in the early days.

An enrollment process that asked the user for permission to adapt was used. purchase PD email lists

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Reading text prompts. This technique might prove to be very useful for power users.

The system allows you to store and preload speaker-specific acoustic models. PD email Profile

This enrollment process is tedious. You should therefore look for more recent information.

Systems rely on speaker adaptive learning methods which first determine the capabilities of the system.

The speaker’s identity is used to adapt the acoustic model based on that assumed identity. Some applications require a wider speaker class, such as gender.

Pre-trained models to be loaded [32]. This is a dictionary.

Language model adaptation is the analysis of the topic or content of spoken input and then used to adapt [33]. Apart from speech recognition,

This technique can also be used to model other components of dialog. Purchasing Department Email List

Different dialog states or keywords that can trigger state switches.

Code switching is i.e. It is impossible to switch the language between utterances. PD consumer email database

Monolingual speech recognition systems can handle this task. There have been efforts to

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Multilingual speech recognition system is being developed [34]. But it seems favorable so far

To design language identification modules that direct speech input.

to the appropriate monolingual recognition software [30]. Idiolect has demonstrated to

Accent has a significant impact on speaker recognition [35].

This has been shown to be detrimental to speech recognition performance. These characteristics have been classified with great care.

and the correct adaptation of system components. We refer to the following:

The reader is urged to [36].

2.4 Summary

This section concludes with a table that summarizes the speaker characteristics most relevant for human-computer and user-centered applications. It also includes references to studies or implementation examples.

thereof. This section does not cover all the applications mentioned.

They are also described in great detail elsewhere in this issue. These include PD consumer email database

53 Speaker Characteristics

Forensic applications where the characteristics gender, age and medical conditions are known.

Language, accent, and sociolect all play an important role. Jessen provides a comprehensive overview of forensic applications in this issue [37]. We did not also discuss Purchasing Department Email List

Emerging applications for home parole, detection and fraud in the

context of Law Enforcement that are concerned with the speaker’s identity

emotion. This article provides an introduction to the field regarding emotion. buy PD targeted email list

Eriksson, in this issue [38].

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Reference to Characteristic Applications

Identity Transaction Authentication [39] ; Access Control [8]

Dialog Systems [14]; Meeting Browser [25] PD business database

Gender Dialog Systems [32]; Speech Synthesis ([3]

Forensics [37]

age Dialog Systems [32]; Forensics [37]

Speech Synthesis [19]

health Forensics [37]

Language Call Routing [15] ; Speech Translation [30]

dialect Forensics [37]

Accent Language Learning [21]; Dialog Systems

Speech Synthesis [19] ; Forensics[37]

Assessment Systems [20]

sociolect Forensics [37]

idiolect Speaker Recognition [35]; Forensics [37]

emotional state Translation [40]; Meeting browser [25]

Law Enforcement [38]; Dialog Systems (18,17]

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Attentional state Human-Robot Interaction [41] Smart Workspaces (26,23,24] Purchasing Department Email List

Relationship/role Translation [31]

Cultural background Dialog Systems [22]

3 What? 3 What?

These are the discrete speaker classes to which vectors for speech features are assigned.

A speaker’s characteristics. These characteristics are relevant for speech-based applications. We have created a hierarchical structure.

As described above.

Figure 1 illustrates the proposal taxonomy. It distinguishes first and foremost between psychological and physiological aspects of speaker characteristics. The

These aspects are further sub-divisioned into those that concern each speaker

Contrary to those that are specific to a community or group. For

A speaker could be a professor at university, a wife or mother to her children, or both. The authority of a PD consumer email database

The context in which the speaker speaks may be different. The hierarchy is determined by the people he/she talks to. Credibility may also depend on the person being done. Purchasing Department Email List

54 T. Schultz

Speaker Characteristics

Psychological

Individual collective identity, gender health, age

Geographic

Background

Attention

State

Emotional

State

Relationship role

language accent dialect

idiolect sociolect

Fig. 1. Taxonomy of Speaker Characteristics

business with, etc. The definition of the group “collective” is required.

A relationship between the sender and the receiver.

This taxonomy has its limitations. It does not include all subjects.

Aspects of an individual (e.g. Weight, height, smoking and drinking habits, demographics like race, income mobility, employment status, or other special aspects

Speech pathologies are an example of this, but instead we should be focusing on the characteristics that we think are important.

To be applicable (and assessable in the context typical speech applications. buy PD targeted email list

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The taxonomy also does not indicate which levels of linguistic information are required to distinguish between different characteristics. This is an example: PD business database

Low-level acoustic features can usually be used to distinguish gender; phonetic, however, is often sufficient.

To discriminate between idiolects, phonological and lexical knowledge may be necessary.

It needs syntactic and semantic information to distinguish sociolects.

To understand the role of speakers, and their role in society, pragmatics may be required. Purchasing Department Email List

Relationship to a group. Low-level physical aspects can be relatively simple

High level cues, which are hard to extract automatically, can be difficult to assess. This is a result.

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Most automatic systems for speaker recognition are still focused on low-level speakers.

cues.

The taxonomy does not include discrimination.

between stable versus transient characteristics. Speaker identity and gender are examples of stable characteristics. Transient characteristics can change over time

time. This aspect could be important for practical applications, particularly if it is a characteristic that underlies dynamic changes over the course of a single period.

audio recording session. Although there are locally stable characteristics like age and health, PD consumer email database

Language, accent, dialect, and even idiolect can change much more slowly than the others. Purchasing Department Email List

Duration of the recording session, characteristics like attentional and emotional

55 Speaker Characteristics

The state of the speaker and the context or topic changing dynamically. The

Over the course of an interaction, the relationship between a speaker and the listener can change. The collective may also have other characteristics, such as sociolect.

The spoken language has many functions, including dialect, accent, and dialect.

It is quite stable within the same language. If a speaker changes languages during a recording session, then the class assignments for accent, idiolect and idiolect will be changed.

Both the dialect and English language can often change. PD email database free

3.1 Language-Dependent Speaker Characteristics

The following outlines the five characteristics language accent dialect.

Language-dependent speaker characteristics include sociolect and idiolect. They are somewhat dependent upon the language used by the speaker.

speaker.

The line between genuinely different languages and dialects of English is drawn

The same language can be the subject of many disputes. We define a dialect as a regional

Modifications at the lexical or grammatical levels are a variant of a language. Accent, on the other hand, is a regional variant that affects only pronunciation. It mainly affects phonetic realizations, but also prosody and allophonic distribution.

Fluency and grammar. For example, British Received Pronunciation is an accent on English

Scottish English, on the other hand, would be considered a dialect because it frequently exhibits

Grammatical differences such as “Are you no going?” or “Aren’t you going?” Purchasing Department Email List

(see [42]). It is assumed that dialects of the same language can be understood by each other. buy PD targeted email list

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Different languages may not be the same, but they are different. Languages need to be learned explicitly, even though they may not be the same.

Language speakers from other languages. Languages also have their own literary characteristics. PD business database

Tradition, dialects are primarily spoken varieties of languages without any literary tradition.

These definitions have been greatly simplified. Many languages do not have a written system, and therefore lack any literary tradition. The distinction between

Languages and dialects are a continuum, not a binary choice. They are often motivated by sociopolitical rather that linguistic considerations. Chinese

Languages, for instance, are unified by a common writing system. However they have their own distinct syntax. PD email database free

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a large number of unintelligible and mutually incompatible varieties, which differ significantly in grammar, vocabulary, pronunciation, and grammar. Most linguists will argue that

These variations can be considered different languages and are officially called dialects.

Encourage the idea of Chinese national unity (see “42”)). The exact opposite

This happened for Serbo­Croatian (the official language of the former Yugoslavia). After

After the split, the languages of Croatian and Serbian were referred to as

Separate languages to highlight national independence Purchasing Department Email List

Languages exhibit sociolectal and idiolectal variations in addition to regional variations.

variation. An idiolect is a speech pattern that has a consistent pronunciation, lexical choice or grammar and which is specific to one speaker. Some idiolectal patterns can include speaker-specific repetitive phrases (e.g. a tendency

To start sentences with Well, to put it simply …), characteristic intonation pattern,

or divergent pronunciations (e.g. nucular instead of nuclear (see [42]). A sociolect is a collection of variations that are typical of a defined group of speakers.

Not by regional cohesion, but by social parameters like economic status and age PD email database free

56 T. Schultz

profession, etc. Some dialects can be considered both a sociolect and a dialect, as they often reflect a specific social status. You can take, for example:

Standard German is very similar to dialects spoken in Hannover or the

state of Saxony–Anhalt, which is the source of Martin Luther’s bible

Translation was the foundation for standard German development. Thus,

Standard German, while being a dialect in these specific areas, is also a sociolect in the sense that it carries a certain prestige as the national language.

Germany is used in all aspects of broadcasting, press and by citizens throughout the country.

higher education.

Despite all the efforts made to make speech recognition systems more robust for real-world applications, regional variations remain a major problem.

challenge. Non-native words can lead to significant increases in word error rates buy PD database for marketing

[43,44] & dialectal speech [45]. This performance is a result of one thing. Purchasing Department Email List

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Degradation is when acoustic models are customized and pronunciation dictionaries are targeted

towards native speakers and lacks the variety that comes from non-native pronunciations. The lexicon as well as the language model do not include dialectal variety. The PD business database

The straightforward solution to deploying accent- or dialect-specific speech recognizers is not possible due to two limitations: a lack of platform resources, and a lack of infrastructure.

Data. Mobile or automotive applications are particularly embedded and restrict the integration of multiple recognizers in one system. Even if

Resources permit the deployment dialect- or accent-specific systems. PD email database free

This results in very limited data resources. Real-world applications therefore require cross-dialect recognition or non-native recognition. Refer to the reader

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For a complete introduction to this area, see [36]. You can find idiolectal features here.

This is used to tailor a speech application for a particular user, such as in training

A speech-based, automated office assistant. Additionally, the software includes idiolectal capabilities.

[35] These features have been proven to be useful in automatic speaker identification. When developing an application, it is also possible to consider sociolectal characteristics. Purchasing Department Email List

an entire user group.

Individual

Accent

Phonetic Lexical

Grammar

k idiolect: l

Language

Collective

k sociolect l

Language k

Fig. 2. Language-dependent Characteristics PD business email database free download

Speaker Characteristics 57

Multilingual environments may have an impact on idiolectal or sociolectal variations. For example, [46] has evidence that bilingual speakers alter their L1 speech after they speak in multilingual settings.

Spending time in a L2-speaking environment. There are many techniques that can improve your speech

Recognition performance in the presence code-switching has been studied [47,48]. The act of using words and phrases from code-switching is known as code-switching.

Multilingual speakers often use different languages in the same sentence.

Engaged in informal conversations

Figure 2 shows the similarities and differences between the languagedependent characteristics language dialect accent, sociolect, idiolect and accent. Main

The effects of linguistic aspects on discriminating factors and whether they are relevant to the individual, are considered discriminating factors.

Individuals and groups can have the same characteristics. Purchasing Department Email List

4 How? 4 How?

Characteristics PD business email database free download

The most prominent and widely studied task in investigating the is probably the.

“Assignment speech features to discrete speakers classes” is speaker recognition

(Who is speaking, class=identity) & language identification (which language).

spoken, class=language). Speech recognition (what is being said, class=content) addresses

This is a larger problem that could be considered part of the “Speaker Classification”

When high-level characteristics such as topic, content, or role are being investigated. As it has become apparent that there are solutions to these three tasks, they have grown closer.

One task might be more beneficial than the other and all three must be done together.

Study of speech-based real-world applications to improve speech quality The following are the results.

We will briefly discuss speaker recognition and language identification. This section buy PD database for marketing

It is not intended to be a complete introduction. For more information, the reader should refer to

Refer to in-depth overviews such as [49] language identification and [39.50] for language classification.

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For speaker recognition. This article is a good introduction to speech recognition. PD customers database

In [51].

4.1 Speaker Recognition

The level of linguistic understanding applied to solving the classification task can help distinguish between different classification approaches. Reynolds identifies a hierarchy perceptual cues humans use to recognize speakers.

[39]. At the highest level, people use semantics and diction.

These are ideosynchrasies that arise from socio-economic status and education.

the place of birth of the speaker. Prosodic features are located on the second level.

The characteristics of personality and parental influence are rhythm, speed and intonation. People are at the lowest level of linguistic communication.

Use the acoustic aspect of sounds such as nasality and breathiness to determine their authenticity. PD business email database free download

Let’s draw some conclusions about the anatomical structure and vocal range of the speaker.

apparatus. Low level physical cues can be extracted automatically. However, it is difficult to determine high-level cues. Most automatic systems are therefore able to detect high-level cues. Purchasing Department Email List

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Systems for speaker recognition still focus on low-level cues.

58 T. Schultz

Conventional systems use Gaussian Mixture Models to capture

frame-level characteristics [52]. GMMs are often unable to distinguish speaker-specific information that changes over multiple frames, as speech frames are presumed to be independent of each other. GMMs do not perform well.

These are able to discriminate speakers based upon higher-level differences such as idiolect. GMMs can also be affected by mismatched acoustics

Conditions that rely solely on low-level speech signal features. To overcome

These problems have led to speaker recognition focusing on higher-level linguistic features such as phonetic information emerging out of speaker ideosynchrasies [35]. This is known as phonetic speaker recognition. It applies relative

Frequency from phone ngrams [53]. This method is being intensively researched [39] and further developed by various modeling strategies, variations in statistical

n-gram models [54], various classifiers such as Support Vector Machines [55], PD business email database free download

Modeling cross-stream dimensions to uncover underlying phone dependencies across multiple language [54,56].

4.2 Language Identification

Language identification methods can be classified in the same way as speaker recognition. This classification is based on the level of linguistic data.

task. [49] distinguishes the signal processing level, and the unit level (e.g. phones),

The sentence level is the word level. These levels allow him to distinguish between spectral features-based acoustic approaches and language identification. Purchasing Department Email List

derived from speech segments [57], Phonotactic approaches that use the contraints relative frequencies of sound unit [58], and other derivatives

Multilingual phone recognizers used as tokenizers [59], extended N-grams [60],

Cross-stream modeling [61], as well as combinations of GMMs, phonotactic and phonotactic model [62]. Navr’atil [49] also lists prosodic approaches that use tone

Intuition, prominence, and prominence [63], as well as those approaches that use full speech buy PD database for marketing

Recognition of language identification [64]

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5 A Classification System For Speaker Characteristics

This section presents a general classification system that applies one framework to the classification and classification of different speaker characteristics. PD customers database

Identity, gender, accent, proficiency, level of attention, and language

speaker. Framework uses high-level phonetic information in order to capture speakers’ ideasynchrasies. This was originally proposed by [58] within the context of language.

Identification and [35] in context of speaker recognition. The idea behind the basic concept is to

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To decode speech using various phone recognizers, and to use the relative frequency of

Phone n-grams are features for training speaker characteristics models and their PD business email database free download

classification. By using more language-independent languages, we enrich existing algorithms with the application of different speaker characteristics.

Phone recognizers and modeling dependencies across multiple phone channels Purchasing Department Email List

[54]. We also investigate the impact of different decision rules and examine their implications.

Speaker Characteristics 59

Consider the number of languages involved and compare multilingual to multi-engineering.

Approaches with respect to classification performance

5.1 Multilingual Phone Sequences

Our experiments were done using the GlobalPhone phone recognition software

Project [65] is available in 12 languages Arabic (AR), Mandarin Chinese(CH),

Croatian (KR), German, French (DE), Japanese (JA), Korean [KO], Portuguese (PO), Russian (“RU”), Spanish (SP), Swedish “SW”) and Turkish (“TU”)

These phone recognizers were trained with the Janus Speech Recognition

Toolkit. The acoustic model is composed of a context-independent, 3-state HMM

16 Gaussians per State in the system. The 13 Mel-scale is the basis of the Gaussians

Cepstral power and coefficients, with first- and second-order derivatives. These are the following

Cepstral subtraction, Linear Discriminant Analysis reduces the input vector PD email database free download

Up to 16 dimensions. Vocal tract length normalization (VTLN), is a part of training.

Normalization of speaker sounds To decode, unsupervised MLLR is used to locate the

Best matching warp factor to the test speaker. The decoding process is done with

Viterbi searches using a fully connected network of monophones null-grammar networks

i.e. The recognition process does not require any prior knowledge of phone statistics.

Figure 3 illustrates the relationship between phone unit count and phone error.

Rates for ten languages Purchasing Department Email List

A language dependent phonetic model (n-gram) is created to train a model for a specific speaker characteristic. It is based on available training data.

We train phonetic bigram models based on the CMUCambridge Statistical language model toolkit [19]. Phonetic bigram models can be directly estimated using the data and not by applying universal background models, or adapting with background models. No transcriptions

Speech data is required for any stage of model training. Figure 4 illustrates the

Procedure for training a speaker identity model speaker k. buy PD database for marketing

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Phone recognizers (PR1 ,…,PRm), decode speaker k’s training data to create m phone strings. These phone strings are used to create m phonetic bigram models. PD customers database

(PM1,k ,…,PMm) are estimates for speaker k.

needs to be classified as one of the following: n-class speaker characteristic, m phone

Recognition software will produce models of m x 10 phonetic bigrams.

Each of the PRi m-phone recognizers, which are used to train phonetic bigram models, decodes the audio segment. Each of the resulting M phone strings is scored against one of the n bigram model PMi,j. This

This results in a perplexity matrix PP. The PPij element of the phonetic bigram model PMi on the phone string output is phone

Recognizer PRi We will investigate other options in future experiments.

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Our default decision algorithm is C* to suggest a class estimate

Select the j

Lowest

i(PP)i,j . This procedure is illustrated in Figure 5. We refer to it as MPM-pp.

The MPM-pp classification method is used in the following.

There are many classification tasks that can be done in relation to speaker characteristics.

Language classification PD email database free download

This section applies the MPM-pp framework for the multiclassification problem in four languages: Japanese, Russian (RU), Spanish and Spanish (SP).

Turkish (TU) We used a limited number of phone recognitions in

Languages other than the four languages of classification are used to duplicate the Purchasing Department Email List

Common circumstances for our identification experiments and to show a

Language independence that is maintained even when the language is identified

65 Speaker Characteristics

Table 8. Confusion matrix for 3-way proficiency classification using 6 versus 7 phone

Recognizers

Phonetic 6 languages 7 langues

model C-1 C-2 C-3 C-1 C-2 C-3

C-1 8 3 19 8 5 17

C-2 8 41 61 6 53 51

C-3 2 12 99 1 20 92

domain. Phone recognizers in Chinese, German, and French

With phone vocabulary sizes of 145 to 47 and 42 respectively, these were borrowed from

The GlobalPhone project. Data for this experiment were also available. PD email database free download

It was borrowed from GlobalPhone but not used for training phone recognizers. It was broken up as shown in Table 9 Training was done with data set 1.

The phonetic models were used, and data set 4 was not available during training.

It is used to assess the performance of the entire classifier. Data

Sets 2 and 3 were used to experiment with different materials.

Decision strategies

Table 9. Table 9.

Set JA RU SP TU

nspk 1-20 20 20 20/20 20

2 5 10 9 10

3 3 5 5 5

4 3 5 4 5 PD database for sale

nutt all 2294 4923 2724 2924

tutt all 6 hrs 9 hrs 8 hrs 7 hrs

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To train the phonetic bigram models, you will need to utterances starting from set 1 in each Lj

Each of the three phone recognition algorithms PRi was used to decode JA, RO, SP, and TU

CH, DE, FR. Twelve trigram models with Kneser/Ney were created PD b2c database

No cut-off or backoff. The size of the training corpora varied from 140K to 500,000. PD email database free download

250K tokens. All 12 models had trigram coverage of 73% to 95%.

Unigram coverage less than 1%

Our lowest average perplexity decision was the first to benchmark accuracy.

rule. We constructed a 4-class multi-classifier using the same principles.

Data set 2 was for each of the durations tk: 5s, 10s and 20s respectively; data set 3 was

Cross-validation

Multi-classifier combines the outputs of multiple binary classifiers. Purchasing Department Email List

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ECOC is error-correcting output coding. A class space that contains 4 languages produces 7 binary partitions. Each of these was taught an independent instructor.

Multilayer Perceptron (MLP), 12 input units, 1 output unit that uses scaled

Conjugate gradients for data set 2 and early stopping with cross-validation

Crossover

Passive crossover networks are built using capacitors, inductors, or both.

Resistors are used to split the audio signal for high-frequency information

goes to the low-frequency information and high-frequency driver PD b2b database

The woofer is the winner. Passive crossovers offer the best value for money

Allow the amplifier to power the loudspeaker

channel. Passive crossovers work in most cases but are not recommended.

They are less precise than active crossovers.

To operate, active crossovers need external power. They can be powered by external power.

The output of a mixer or preamplifier is connected to the output of an amplifier.

Power amplifier. Active crossover has the advantage of being able to control power amplifiers.

You can change many characteristics, such as the crossover frequency.

The rate of transition.

A active crossover is usually an additional component you will need

To purchase. Check that your loudspeaker system is certified.

Separate inputs are needed to accommodate multiple amplifiers.

Your crossover. An especially cost-effective method to obtain an

Active crossover can be achieved by purchasing a Digital Signal Processor, (DSP).

You can create equalizers and crossovers.

Woofer

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The signal is sent through a loudspeaker…or more.

The signal chain contains the audio signal.

Use an instrument or microphone to communicate your message. Purchasing Department Email List

The Signal Chain

A sound system is a system that transmits an audio signal from its source to the system components. These components balance, process and amplify the signal.

A loudspeaker, or headphones, is released. The signal chain is the audio signal’s path.

Some signal chains can be complex with many components and divergences. Others are simple. Below is a diagram of a signal chain.

Basic, stripped-down signal chains

What Does an Audio System Look Like?

(c) 2012 Bosch Security Systems 10.

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Signal voltage refers to the value of the signal being sent to an audio device. We usually refer to the value in decibels volts (dBV).

Each microphone, amplifier, pickup, and source of program has a unique voltage level. However, the signals cannot be mixed until they are all combined. PD b2c database

They are all promoted to “line” professional status.

Mic level is the relatively

Low-level signal (generally)

From -40 to -60 dBV (of a)

Microphone that must be

Amplified to line-level,

It is easier to manipulate

A mixing console.

 

Different levels of signals

Enter a mixer, but make sure the signal is clear

Professionals can take your leaves.

line level. The standard is

Audio: +4 dBu audio or -10 dBV

Levels, or about 1V.

We need stronger signals

to drive loudspeakers.

Amplifiers boost processed

Signals to the speaker level Purchasing Department Email List

So the loudspeakers are rewarded

There is enough power to get the job done.

(c) 2012 Bosch Security Systems

11

Coverage Pattern is a directional pattern for a loudspeaker system that can be varied by frequency or tone. One example is a loudspeaker’s

One output may cover a large and narrow area of a room while another might cover an equally long and tall section. PD b2b database

Pro Tip:

Make sure your loudspeakers cover your entire audience. People who are unable to hear you clearly are more likely not to be able to hear you.

Throw things on the stage.

Attachment: A cabinet that houses loudspeaker drivers as well as associated electronic hardware such crossover circuits. An

An enclosure can be as simple as a wooden box, or it could be complex and contain ports, baffles and acoustic insulation. The enclosure

This prevents sound waves from interfering between the rear and front, reduces vibration and heat generation.

Driver coils. It also shapes the low frequency response. To do this, the enclosure design must be compatible with the driver characteristics.

This was achieved with great success. Electro-Voice was the first company to implement the concept of matching enclosure designs systematically.

Drivers should be able to extend the low end as much as possible. Purchasing Department Email List

Frequency Response is a measurement of how well a speaker or electronic component reproduces sounds. Frequency: A frequency

Response specification refers to the frequency range and deviation from a perfectly flat response. PD b2b database

Frequency response curves can be used to show the accuracy of sound systems.

Frequency Range is the lower and higher limits of the system’s output. It is not possible for a loudspeaker to reproduce the sound of another speaker.

Anything below or over its rated frequency range is unacceptable, Buddy.

Pro Tip:

Different speaker manufacturers may use different standards. To make an accurate comparison, be sure to read the manufacturer’s footnotes.

The frequency range of a frequency measured at the -10 dB points will usually be larger than that measured at the 3 dB points. However, it is not fair.

Considerations when choosing a loudspeaker

Here are some helpful terms

You’ll be able to explore your options for loudspeakers and you’ll see many fancy terms such as frequency, impedance, and frequency.

Response, power rating, SPL. They are, however, very important. Here’s a quick primer.

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Impedance (Z), The resistance that an electronic circuit or device offers to the AC current flowing through it

It. It is often represented by the mathematical symbol Z and measured in Ohms. Impedance Purchasing Department Email List

This article describes the difficulty of a speaker to drive and its compatibility for various amplifiers. PD database for sale

Human-Centered Systems are computer services that are implicitly, indirectly, and unobstructed to people whose primary goal it to interact

With other people. Computers are invisible – they act as electronic butlers. email marketing database PD

It is a way to anticipate and meet people’s needs. Computers are thus introduced to a network of people interfacing with each other, instead of condemning.

Humans can operate in a loop computerized environment (see CHIL – Computers and the Human)

Interaction Loop [23] Purchasing Department Email List

Smart Room Environments are a new category of computer services.

Computers monitor and interpret interactions and actions of people to help them communicate better. An example of an implementation is an automatic meeting support system. It tracks what was said, to whom and how.

It was [25]. Annotating speech recognition outputs with speakers’ PD email Profile

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The meeting notes can be properly indexed and skimmed. They can also be searched and retrieved for identity, attention, and emotion. Sociallysupportive workspaces [23] and augmented multiparty interactions (26] encourage cooperation among participants in meetings, with multimodal interfaces to enter. purchase PD email lists

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Facilitate functionalities and manipulate the contributions of participants

Monitor group activities. Other services that are offered within the framework PD email Profile

CHIL [23] includes better ways to connect people and support human memory. Computers must automatically collect context-aware information, such as meeting type, topic, and environmental, in order to provide these services.

conditions and participant characteristics, such as attentional state.

Speech Translation is an example of computer-mediated software that supports human-to-human communication [27,28 and 29]. Speech is a task.

Translation is the recognition of incoming speech in the source language. The translator output is translated into the target language text.

Synthesize the translated text into audible speech in target language. Most email marketing database PD

Applications are designed in two-directional one-directional systems. Some systems may be configured as such. Purchasing Department Email List

Automatic language identification is used to route the speech into the appropriate system [30]. The translation should preserve the original meaning of the spoken input but also reflect other aspects.

These include politeness, respect and directness. These are just a few of the many aspects.

This could be directly derived by speaker characteristics such as generation

Synthesized output that is appropriate based on speaker’s gender or other relevant factors.

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The identification of an individual’s emotional state in order to interpret and communicate emotions. Some aspects of the human body are also important.

Relationship between the speaker/listener In some languages, the word “word” is used.

The hierarchy between the sender and the receiver can affect the usage of the form. In this case, the wrong form could offend the receiver. Japanese is one example of such an.

52 T. Schultz

For example, Dr. Sadaoki Tomuko could be addressed as Tomukosan if he is

If the sender is not the boss, a friend or Tomukosensei should be contacted. This is how to address it email marketing database PD

Problem solved, the English-Japanese JANUS Translation System [31] was created to address this problem. Purchasing Department Email List

You can switch between politeness levels.

2.3 Adaptation to System Components

The classification of speaker characteristics is a critical component, as we have already discussed.

role in personalization and customization of applications. Speakers can also be used to customize and personalize applications.

To adapt components of the system to specific voice characteristics, it is necessary to assess the characteristics.

The content of the speech and the speaker. This adaptation has been completed.

It has been shown to significantly improve recognition accuracy.

Compared favorably to overall system performance.

Traditional speech recognition adaptation is mostly concerned with the

The acoustic model and the language model can be adapted. The acoustic model was used in the early days.

An enrollment process that asked the user for permission to adapt was used. purchase PD email lists

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Reading text prompts. This technique might prove to be very useful for power users.

The system allows you to store and preload speaker-specific acoustic models. PD email Profile

This enrollment process is tedious. You should therefore look for more recent information.

Systems rely on speaker adaptive learning methods which first determine the capabilities of the system.

The speaker’s identity is used to adapt the acoustic model based on that assumed identity. Some applications require a wider speaker class, such as gender.

Pre-trained models to be loaded [32]. This is a dictionary.

Language model adaptation is the analysis of the topic or content of spoken input and then used to adapt [33]. Apart from speech recognition,

This technique can also be used to model other components of dialog. Purchasing Department Email List

Different dialog states or keywords that can trigger state switches.

Code switching is i.e. It is impossible to switch the language between utterances. PD consumer email database

Monolingual speech recognition systems can handle this task. There have been efforts to

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Multilingual speech recognition system is being developed [34]. But it seems favorable so far

To design language identification modules that direct speech input.

to the appropriate monolingual recognition software [30]. Idiolect has demonstrated to

Accent has a significant impact on speaker recognition [35].

This has been shown to be detrimental to speech recognition performance. These characteristics have been classified with great care.

and the correct adaptation of system components. We refer to the following:

The reader is urged to [36].

2.4 Summary

This section concludes with a table that summarizes the speaker characteristics most relevant for human-computer and user-centered applications. It also includes references to studies or implementation examples.

thereof. This section does not cover all the applications mentioned.

They are also described in great detail elsewhere in this issue. These include PD consumer email database

53 Speaker Characteristics

Forensic applications where the characteristics gender, age and medical conditions are known.

Language, accent, and sociolect all play an important role. Jessen provides a comprehensive overview of forensic applications in this issue [37]. We did not also discuss Purchasing Department Email List

Emerging applications for home parole, detection and fraud in the

context of Law Enforcement that are concerned with the speaker’s identity

emotion. This article provides an introduction to the field regarding emotion. buy PD targeted email list

Eriksson, in this issue [38].

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Reference to Characteristic Applications

Identity Transaction Authentication [39] ; Access Control [8]

Dialog Systems [14]; Meeting Browser [25] PD business database

Gender Dialog Systems [32]; Speech Synthesis ([3]

Forensics [37]

age Dialog Systems [32]; Forensics [37]

Speech Synthesis [19]

health Forensics [37]

Language Call Routing [15] ; Speech Translation [30]

dialect Forensics [37]

Accent Language Learning [21]; Dialog Systems

Speech Synthesis [19] ; Forensics[37]

Assessment Systems [20]

sociolect Forensics [37]

idiolect Speaker Recognition [35]; Forensics [37]

emotional state Translation [40]; Meeting browser [25]

Law Enforcement [38]; Dialog Systems (18,17]

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Attentional state Human-Robot Interaction [41] Smart Workspaces (26,23,24] Purchasing Department Email List

Relationship/role Translation [31]

Cultural background Dialog Systems [22]

3 What? 3 What?

These are the discrete speaker classes to which vectors for speech features are assigned.

A speaker’s characteristics. These characteristics are relevant for speech-based applications. We have created a hierarchical structure.

As described above.

Figure 1 illustrates the proposal taxonomy. It distinguishes first and foremost between psychological and physiological aspects of speaker characteristics. The

These aspects are further sub-divisioned into those that concern each speaker

Contrary to those that are specific to a community or group. For

A speaker could be a professor at university, a wife or mother to her children, or both. The authority of a PD consumer email database

The context in which the speaker speaks may be different. The hierarchy is determined by the people he/she talks to. Credibility may also depend on the person being done. Purchasing Department Email List

54 T. Schultz

Speaker Characteristics

Psychological

Individual collective identity, gender health, age

Geographic

Background

Attention

State

Emotional

State

Relationship role

language accent dialect

idiolect sociolect

Fig. 1. Taxonomy of Speaker Characteristics

business with, etc. The definition of the group “collective” is required.

A relationship between the sender and the receiver.

This taxonomy has its limitations. It does not include all subjects.

Aspects of an individual (e.g. Weight, height, smoking and drinking habits, demographics like race, income mobility, employment status, or other special aspects

Speech pathologies are an example of this, but instead we should be focusing on the characteristics that we think are important.

To be applicable (and assessable in the context typical speech applications. buy PD targeted email list

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The taxonomy also does not indicate which levels of linguistic information are required to distinguish between different characteristics. This is an example: PD business database

Low-level acoustic features can usually be used to distinguish gender; phonetic, however, is often sufficient.

To discriminate between idiolects, phonological and lexical knowledge may be necessary.

It needs syntactic and semantic information to distinguish sociolects.

To understand the role of speakers, and their role in society, pragmatics may be required. Purchasing Department Email List

Relationship to a group. Low-level physical aspects can be relatively simple

High level cues, which are hard to extract automatically, can be difficult to assess. This is a result.

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Most automatic systems for speaker recognition are still focused on low-level speakers.

cues.

The taxonomy does not include discrimination.

between stable versus transient characteristics. Speaker identity and gender are examples of stable characteristics. Transient characteristics can change over time

time. This aspect could be important for practical applications, particularly if it is a characteristic that underlies dynamic changes over the course of a single period.

audio recording session. Although there are locally stable characteristics like age and health, PD consumer email database

Language, accent, dialect, and even idiolect can change much more slowly than the others. Purchasing Department Email List

Duration of the recording session, characteristics like attentional and emotional

55 Speaker Characteristics

The state of the speaker and the context or topic changing dynamically. The

Over the course of an interaction, the relationship between a speaker and the listener can change. The collective may also have other characteristics, such as sociolect.

The spoken language has many functions, including dialect, accent, and dialect.

It is quite stable within the same language. If a speaker changes languages during a recording session, then the class assignments for accent, idiolect and idiolect will be changed.

Both the dialect and English language can often change. PD email database free

3.1 Language-Dependent Speaker Characteristics

The following outlines the five characteristics language accent dialect.

Language-dependent speaker characteristics include sociolect and idiolect. They are somewhat dependent upon the language used by the speaker.

speaker.

The line between genuinely different languages and dialects of English is drawn

The same language can be the subject of many disputes. We define a dialect as a regional

Modifications at the lexical or grammatical levels are a variant of a language. Accent, on the other hand, is a regional variant that affects only pronunciation. It mainly affects phonetic realizations, but also prosody and allophonic distribution.

Fluency and grammar. For example, British Received Pronunciation is an accent on English

Scottish English, on the other hand, would be considered a dialect because it frequently exhibits

Grammatical differences such as “Are you no going?” or “Aren’t you going?” Purchasing Department Email List

(see [42]). It is assumed that dialects of the same language can be understood by each other. buy PD targeted email list

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Different languages may not be the same, but they are different. Languages need to be learned explicitly, even though they may not be the same.

Language speakers from other languages. Languages also have their own literary characteristics. PD business database

Tradition, dialects are primarily spoken varieties of languages without any literary tradition.

These definitions have been greatly simplified. Many languages do not have a written system, and therefore lack any literary tradition. The distinction between

Languages and dialects are a continuum, not a binary choice. They are often motivated by sociopolitical rather that linguistic considerations. Chinese

Languages, for instance, are unified by a common writing system. However they have their own distinct syntax. PD email database free

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a large number of unintelligible and mutually incompatible varieties, which differ significantly in grammar, vocabulary, pronunciation, and grammar. Most linguists will argue that

These variations can be considered different languages and are officially called dialects.

Encourage the idea of Chinese national unity (see “42”)). The exact opposite

This happened for Serbo­Croatian (the official language of the former Yugoslavia). After

After the split, the languages of Croatian and Serbian were referred to as

Separate languages to highlight national independence Purchasing Department Email List

Languages exhibit sociolectal and idiolectal variations in addition to regional variations.

variation. An idiolect is a speech pattern that has a consistent pronunciation, lexical choice or grammar and which is specific to one speaker. Some idiolectal patterns can include speaker-specific repetitive phrases (e.g. a tendency

To start sentences with Well, to put it simply …), characteristic intonation pattern,

or divergent pronunciations (e.g. nucular instead of nuclear (see [42]). A sociolect is a collection of variations that are typical of a defined group of speakers.

Not by regional cohesion, but by social parameters like economic status and age PD email database free

56 T. Schultz

profession, etc. Some dialects can be considered both a sociolect and a dialect, as they often reflect a specific social status. You can take, for example:

Standard German is very similar to dialects spoken in Hannover or the

state of Saxony–Anhalt, which is the source of Martin Luther’s bible

Translation was the foundation for standard German development. Thus,

Standard German, while being a dialect in these specific areas, is also a sociolect in the sense that it carries a certain prestige as the national language.

Germany is used in all aspects of broadcasting, press and by citizens throughout the country.

higher education.

Despite all the efforts made to make speech recognition systems more robust for real-world applications, regional variations remain a major problem.

challenge. Non-native words can lead to significant increases in word error rates buy PD database for marketing

[43,44] & dialectal speech [45]. This performance is a result of one thing. Purchasing Department Email List

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Degradation is when acoustic models are customized and pronunciation dictionaries are targeted

towards native speakers and lacks the variety that comes from non-native pronunciations. The lexicon as well as the language model do not include dialectal variety. The PD business database

The straightforward solution to deploying accent- or dialect-specific speech recognizers is not possible due to two limitations: a lack of platform resources, and a lack of infrastructure.

Data. Mobile or automotive applications are particularly embedded and restrict the integration of multiple recognizers in one system. Even if

Resources permit the deployment dialect- or accent-specific systems. PD email database free

This results in very limited data resources. Real-world applications therefore require cross-dialect recognition or non-native recognition. Refer to the reader

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For a complete introduction to this area, see [36]. You can find idiolectal features here.

This is used to tailor a speech application for a particular user, such as in training

A speech-based, automated office assistant. Additionally, the software includes idiolectal capabilities.

[35] These features have been proven to be useful in automatic speaker identification. When developing an application, it is also possible to consider sociolectal characteristics. Purchasing Department Email List

an entire user group.

Individual

Accent

Phonetic Lexical

Grammar

k idiolect: l

Language

Collective

k sociolect l

Language k

Fig. 2. Language-dependent Characteristics PD business email database free download

Speaker Characteristics 57

Multilingual environments may have an impact on idiolectal or sociolectal variations. For example, [46] has evidence that bilingual speakers alter their L1 speech after they speak in multilingual settings.

Spending time in a L2-speaking environment. There are many techniques that can improve your speech

Recognition performance in the presence code-switching has been studied [47,48]. The act of using words and phrases from code-switching is known as code-switching.

Multilingual speakers often use different languages in the same sentence.

Engaged in informal conversations

Figure 2 shows the similarities and differences between the languagedependent characteristics language dialect accent, sociolect, idiolect and accent. Main

The effects of linguistic aspects on discriminating factors and whether they are relevant to the individual, are considered discriminating factors.

Individuals and groups can have the same characteristics. Purchasing Department Email List

4 How? 4 How?

Characteristics PD business email database free download

The most prominent and widely studied task in investigating the is probably the.

“Assignment speech features to discrete speakers classes” is speaker recognition

(Who is speaking, class=identity) & language identification (which language).

spoken, class=language). Speech recognition (what is being said, class=content) addresses

This is a larger problem that could be considered part of the “Speaker Classification”

When high-level characteristics such as topic, content, or role are being investigated. As it has become apparent that there are solutions to these three tasks, they have grown closer.

One task might be more beneficial than the other and all three must be done together.

Study of speech-based real-world applications to improve speech quality The following are the results.

We will briefly discuss speaker recognition and language identification. This section buy PD database for marketing

It is not intended to be a complete introduction. For more information, the reader should refer to

Refer to in-depth overviews such as [49] language identification and [39.50] for language classification.

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For speaker recognition. This article is a good introduction to speech recognition. PD customers database

In [51].

4.1 Speaker Recognition

The level of linguistic understanding applied to solving the classification task can help distinguish between different classification approaches. Reynolds identifies a hierarchy perceptual cues humans use to recognize speakers.

[39]. At the highest level, people use semantics and diction.

These are ideosynchrasies that arise from socio-economic status and education.

the place of birth of the speaker. Prosodic features are located on the second level.

The characteristics of personality and parental influence are rhythm, speed and intonation. People are at the lowest level of linguistic communication.

Use the acoustic aspect of sounds such as nasality and breathiness to determine their authenticity. PD business email database free download

Let’s draw some conclusions about the anatomical structure and vocal range of the speaker.

apparatus. Low level physical cues can be extracted automatically. However, it is difficult to determine high-level cues. Most automatic systems are therefore able to detect high-level cues. Purchasing Department Email List

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Systems for speaker recognition still focus on low-level cues.

58 T. Schultz

Conventional systems use Gaussian Mixture Models to capture

frame-level characteristics [52]. GMMs are often unable to distinguish speaker-specific information that changes over multiple frames, as speech frames are presumed to be independent of each other. GMMs do not perform well.

These are able to discriminate speakers based upon higher-level differences such as idiolect. GMMs can also be affected by mismatched acoustics

Conditions that rely solely on low-level speech signal features. To overcome

These problems have led to speaker recognition focusing on higher-level linguistic features such as phonetic information emerging out of speaker ideosynchrasies [35]. This is known as phonetic speaker recognition. It applies relative

Frequency from phone ngrams [53]. This method is being intensively researched [39] and further developed by various modeling strategies, variations in statistical

n-gram models [54], various classifiers such as Support Vector Machines [55], PD business email database free download

Modeling cross-stream dimensions to uncover underlying phone dependencies across multiple language [54,56].

4.2 Language Identification

Language identification methods can be classified in the same way as speaker recognition. This classification is based on the level of linguistic data.

task. [49] distinguishes the signal processing level, and the unit level (e.g. phones),

The sentence level is the word level. These levels allow him to distinguish between spectral features-based acoustic approaches and language identification. Purchasing Department Email List

derived from speech segments [57], Phonotactic approaches that use the contraints relative frequencies of sound unit [58], and other derivatives

Multilingual phone recognizers used as tokenizers [59], extended N-grams [60],

Cross-stream modeling [61], as well as combinations of GMMs, phonotactic and phonotactic model [62]. Navr’atil [49] also lists prosodic approaches that use tone

Intuition, prominence, and prominence [63], as well as those approaches that use full speech buy PD database for marketing

Recognition of language identification [64]

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5 A Classification System For Speaker Characteristics

This section presents a general classification system that applies one framework to the classification and classification of different speaker characteristics. PD customers database

Identity, gender, accent, proficiency, level of attention, and language

speaker. Framework uses high-level phonetic information in order to capture speakers’ ideasynchrasies. This was originally proposed by [58] within the context of language.

Identification and [35] in context of speaker recognition. The idea behind the basic concept is to

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To decode speech using various phone recognizers, and to use the relative frequency of

Phone n-grams are features for training speaker characteristics models and their PD business email database free download

classification. By using more language-independent languages, we enrich existing algorithms with the application of different speaker characteristics.

Phone recognizers and modeling dependencies across multiple phone channels Purchasing Department Email List

[54]. We also investigate the impact of different decision rules and examine their implications.

Speaker Characteristics 59

Consider the number of languages involved and compare multilingual to multi-engineering.

Approaches with respect to classification performance

5.1 Multilingual Phone Sequences

Our experiments were done using the GlobalPhone phone recognition software

Project [65] is available in 12 languages Arabic (AR), Mandarin Chinese(CH),

Croatian (KR), German, French (DE), Japanese (JA), Korean [KO], Portuguese (PO), Russian (“RU”), Spanish (SP), Swedish “SW”) and Turkish (“TU”)

These phone recognizers were trained with the Janus Speech Recognition

Toolkit. The acoustic model is composed of a context-independent, 3-state HMM

16 Gaussians per State in the system. The 13 Mel-scale is the basis of the Gaussians

Cepstral power and coefficients, with first- and second-order derivatives. These are the following

Cepstral subtraction, Linear Discriminant Analysis reduces the input vector PD email database free download

Up to 16 dimensions. Vocal tract length normalization (VTLN), is a part of training.

Normalization of speaker sounds To decode, unsupervised MLLR is used to locate the

Best matching warp factor to the test speaker. The decoding process is done with

Viterbi searches using a fully connected network of monophones null-grammar networks

i.e. The recognition process does not require any prior knowledge of phone statistics.

Figure 3 illustrates the relationship between phone unit count and phone error.

Rates for ten languages Purchasing Department Email List

A language dependent phonetic model (n-gram) is created to train a model for a specific speaker characteristic. It is based on available training data.

We train phonetic bigram models based on the CMUCambridge Statistical language model toolkit [19]. Phonetic bigram models can be directly estimated using the data and not by applying universal background models, or adapting with background models. No transcriptions

Speech data is required for any stage of model training. Figure 4 illustrates the

Procedure for training a speaker identity model speaker k. buy PD database for marketing

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Phone recognizers (PR1 ,…,PRm), decode speaker k’s training data to create m phone strings. These phone strings are used to create m phonetic bigram models. PD customers database

(PM1,k ,…,PMm) are estimates for speaker k.

needs to be classified as one of the following: n-class speaker characteristic, m phone

Recognition software will produce models of m x 10 phonetic bigrams.

Each of the PRi m-phone recognizers, which are used to train phonetic bigram models, decodes the audio segment. Each of the resulting M phone strings is scored against one of the n bigram model PMi,j. This

This results in a perplexity matrix PP. The PPij element of the phonetic bigram model PMi on the phone string output is phone

Recognizer PRi We will investigate other options in future experiments.

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Our default decision algorithm is C* to suggest a class estimate

Select the j

Lowest

i(PP)i,j . This procedure is illustrated in Figure 5. We refer to it as MPM-pp.

The MPM-pp classification method is used in the following.

There are many classification tasks that can be done in relation to speaker characteristics.

Language classification PD email database free download

This section applies the MPM-pp framework for the multiclassification problem in four languages: Japanese, Russian (RU), Spanish and Spanish (SP).

Turkish (TU) We used a limited number of phone recognitions in

Languages other than the four languages of classification are used to duplicate the Purchasing Department Email List

Common circumstances for our identification experiments and to show a

Language independence that is maintained even when the language is identified

65 Speaker Characteristics

Table 8. Confusion matrix for 3-way proficiency classification using 6 versus 7 phone

Recognizers

Phonetic 6 languages 7 langues

model C-1 C-2 C-3 C-1 C-2 C-3

C-1 8 3 19 8 5 17

C-2 8 41 61 6 53 51

C-3 2 12 99 1 20 92

domain. Phone recognizers in Chinese, German, and French

With phone vocabulary sizes of 145 to 47 and 42 respectively, these were borrowed from

The GlobalPhone project. Data for this experiment were also available. PD email database free download

It was borrowed from GlobalPhone but not used for training phone recognizers. It was broken up as shown in Table 9 Training was done with data set 1.

The phonetic models were used, and data set 4 was not available during training.

It is used to assess the performance of the entire classifier. Data

Sets 2 and 3 were used to experiment with different materials.

Decision strategies

Table 9. Table 9.

Set JA RU SP TU

nspk 1-20 20 20 20/20 20

2 5 10 9 10

3 3 5 5 5

4 3 5 4 5 PD database for sale

nutt all 2294 4923 2724 2924

tutt all 6 hrs 9 hrs 8 hrs 7 hrs

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To train the phonetic bigram models, you will need to utterances starting from set 1 in each Lj

Each of the three phone recognition algorithms PRi was used to decode JA, RO, SP, and TU

CH, DE, FR. Twelve trigram models with Kneser/Ney were created PD b2c database

No cut-off or backoff. The size of the training corpora varied from 140K to 500,000. PD email database free download

250K tokens. All 12 models had trigram coverage of 73% to 95%.

Unigram coverage less than 1%

Our lowest average perplexity decision was the first to benchmark accuracy.

rule. We constructed a 4-class multi-classifier using the same principles.

Data set 2 was for each of the durations tk: 5s, 10s and 20s respectively; data set 3 was

Cross-validation

Multi-classifier combines the outputs of multiple binary classifiers. Purchasing Department Email List

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ECOC is error-correcting output coding. A class space that contains 4 languages produces 7 binary partitions. Each of these was taught an independent instructor.

Multilayer Perceptron (MLP), 12 input units, 1 output unit that uses scaled

Conjugate gradients for data set 2 and early stopping with cross-validation

Crossover

Passive crossover networks are built using capacitors, inductors, or both.

Resistors are used to split the audio signal for high-frequency information

goes to the low-frequency information and high-frequency driver PD b2b database

The woofer is the winner. Passive crossovers offer the best value for money

Allow the amplifier to power the loudspeaker

channel. Passive crossovers work in most cases but are not recommended.

They are less precise than active crossovers.

To operate, active crossovers need external power. They can be powered by external power.

The output of a mixer or preamplifier is connected to the output of an amplifier.

Power amplifier. Active crossover has the advantage of being able to control power amplifiers.

You can change many characteristics, such as the crossover frequency.

The rate of transition.

A active crossover is usually an additional component you will need

To purchase. Check that your loudspeaker system is certified.

Separate inputs are needed to accommodate multiple amplifiers.

Your crossover. An especially cost-effective method to obtain an

Active crossover can be achieved by purchasing a Digital Signal Processor, (DSP).

You can create equalizers and crossovers.

Woofer

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The signal is sent through a loudspeaker…or more.

The signal chain contains the audio signal.

Use an instrument or microphone to communicate your message. Purchasing Department Email List

The Signal Chain

A sound system is a system that transmits an audio signal from its source to the system components. These components balance, process and amplify the signal.

A loudspeaker, or headphones, is released. The signal chain is the audio signal’s path.

Some signal chains can be complex with many components and divergences. Others are simple. Below is a diagram of a signal chain.

Basic, stripped-down signal chains

What Does an Audio System Look Like?

(c) 2012 Bosch Security Systems 10.

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Signal voltage refers to the value of the signal being sent to an audio device. We usually refer to the value in decibels volts (dBV).

Each microphone, amplifier, pickup, and source of program has a unique voltage level. However, the signals cannot be mixed until they are all combined. PD b2c database

They are all promoted to “line” professional status.

Mic level is the relatively

Low-level signal (generally)

From -40 to -60 dBV (of a)

Microphone that must be

Amplified to line-level,

It is easier to manipulate

A mixing console.

 

Different levels of signals

Enter a mixer, but make sure the signal is clear

Professionals can take your leaves.

line level. The standard is

Audio: +4 dBu audio or -10 dBV

Levels, or about 1V.

We need stronger signals

to drive loudspeakers.

Amplifiers boost processed

Signals to the speaker level Purchasing Department Email List

So the loudspeakers are rewarded

There is enough power to get the job done.

(c) 2012 Bosch Security Systems

11

Coverage Pattern is a directional pattern for a loudspeaker system that can be varied by frequency or tone. One example is a loudspeaker’s

One output may cover a large and narrow area of a room while another might cover an equally long and tall section. PD b2b database

Pro Tip:

Make sure your loudspeakers cover your entire audience. People who are unable to hear you clearly are more likely not to be able to hear you.

Throw things on the stage.

Attachment: A cabinet that houses loudspeaker drivers as well as associated electronic hardware such crossover circuits. An

An enclosure can be as simple as a wooden box, or it could be complex and contain ports, baffles and acoustic insulation. The enclosure

This prevents sound waves from interfering between the rear and front, reduces vibration and heat generation.

Driver coils. It also shapes the low frequency response. To do this, the enclosure design must be compatible with the driver characteristics.

This was achieved with great success. Electro-Voice was the first company to implement the concept of matching enclosure designs systematically.

Drivers should be able to extend the low end as much as possible. Purchasing Department Email List

Frequency Response is a measurement of how well a speaker or electronic component reproduces sounds. Frequency: A frequency

Response specification refers to the frequency range and deviation from a perfectly flat response. PD b2b database

Frequency response curves can be used to show the accuracy of sound systems.

Frequency Range is the lower and higher limits of the system’s output. It is not possible for a loudspeaker to reproduce the sound of another speaker.

Anything below or over its rated frequency range is unacceptable, Buddy.

Pro Tip:

Different speaker manufacturers may use different standards. To make an accurate comparison, be sure to read the manufacturer’s footnotes.

The frequency range of a frequency measured at the -10 dB points will usually be larger than that measured at the 3 dB points. However, it is not fair.

Considerations when choosing a loudspeaker

Here are some helpful terms

You’ll be able to explore your options for loudspeakers and you’ll see many fancy terms such as frequency, impedance, and frequency.

Response, power rating, SPL. They are, however, very important. Here’s a quick primer.

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Impedance (Z), The resistance that an electronic circuit or device offers to the AC current flowing through it

It. It is often represented by the mathematical symbol Z and measured in Ohms. Impedance Purchasing Department Email List

This article describes the difficulty of a speaker to drive and its compatibility for various amplifiers. PD database for sale

Human-Centered Systems are computer services that are implicitly, indirectly, and unobstructed to people whose primary goal it to interact

With other people. Computers are invisible – they act as electronic butlers. email marketing database PD

It is a way to anticipate and meet people’s needs. Computers are thus introduced to a network of people interfacing with each other, instead of condemning.

Humans can operate in a loop computerized environment (see CHIL – Computers and the Human)

Interaction Loop [23] Purchasing Department Email List

Smart Room Environments are a new category of computer services.

Computers monitor and interpret interactions and actions of people to help them communicate better. An example of an implementation is an automatic meeting support system. It tracks what was said, to whom and how.

It was [25]. Annotating speech recognition outputs with speakers’ PD email Profile

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The meeting notes can be properly indexed and skimmed. They can also be searched and retrieved for identity, attention, and emotion. Sociallysupportive workspaces [23] and augmented multiparty interactions (26] encourage cooperation among participants in meetings, with multimodal interfaces to enter. purchase PD email lists

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Facilitate functionalities and manipulate the contributions of participants

Monitor group activities. Other services that are offered within the framework PD email Profile

CHIL [23] includes better ways to connect people and support human memory. Computers must automatically collect context-aware information, such as meeting type, topic, and environmental, in order to provide these services.

conditions and participant characteristics, such as attentional state.

Speech Translation is an example of computer-mediated software that supports human-to-human communication [27,28 and 29]. Speech is a task.

Translation is the recognition of incoming speech in the source language. The translator output is translated into the target language text.

Synthesize the translated text into audible speech in target language. Most email marketing database PD

Applications are designed in two-directional one-directional systems. Some systems may be configured as such. Purchasing Department Email List

Automatic language identification is used to route the speech into the appropriate system [30]. The translation should preserve the original meaning of the spoken input but also reflect other aspects.

These include politeness, respect and directness. These are just a few of the many aspects.

This could be directly derived by speaker characteristics such as generation

Synthesized output that is appropriate based on speaker’s gender or other relevant factors.

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The identification of an individual’s emotional state in order to interpret and communicate emotions. Some aspects of the human body are also important.

Relationship between the speaker/listener In some languages, the word “word” is used.

The hierarchy between the sender and the receiver can affect the usage of the form. In this case, the wrong form could offend the receiver. Japanese is one example of such an.

52 T. Schultz

For example, Dr. Sadaoki Tomuko could be addressed as Tomukosan if he is

If the sender is not the boss, a friend or Tomukosensei should be contacted. This is how to address it email marketing database PD

Problem solved, the English-Japanese JANUS Translation System [31] was created to address this problem. Purchasing Department Email List

You can switch between politeness levels.

2.3 Adaptation to System Components

The classification of speaker characteristics is a critical component, as we have already discussed.

role in personalization and customization of applications. Speakers can also be used to customize and personalize applications.

To adapt components of the system to specific voice characteristics, it is necessary to assess the characteristics.

The content of the speech and the speaker. This adaptation has been completed.

It has been shown to significantly improve recognition accuracy.

Compared favorably to overall system performance.

Traditional speech recognition adaptation is mostly concerned with the

The acoustic model and the language model can be adapted. The acoustic model was used in the early days.

An enrollment process that asked the user for permission to adapt was used. purchase PD email lists

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Reading text prompts. This technique might prove to be very useful for power users.

The system allows you to store and preload speaker-specific acoustic models. PD email Profile

This enrollment process is tedious. You should therefore look for more recent information.

Systems rely on speaker adaptive learning methods which first determine the capabilities of the system.

The speaker’s identity is used to adapt the acoustic model based on that assumed identity. Some applications require a wider speaker class, such as gender.

Pre-trained models to be loaded [32]. This is a dictionary.

Language model adaptation is the analysis of the topic or content of spoken input and then used to adapt [33]. Apart from speech recognition,

This technique can also be used to model other components of dialog. Purchasing Department Email List

Different dialog states or keywords that can trigger state switches.

Code switching is i.e. It is impossible to switch the language between utterances. PD consumer email database

Monolingual speech recognition systems can handle this task. There have been efforts to

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Multilingual speech recognition system is being developed [34]. But it seems favorable so far

To design language identification modules that direct speech input.

to the appropriate monolingual recognition software [30]. Idiolect has demonstrated to

Accent has a significant impact on speaker recognition [35].

This has been shown to be detrimental to speech recognition performance. These characteristics have been classified with great care.

and the correct adaptation of system components. We refer to the following:

The reader is urged to [36].

2.4 Summary

This section concludes with a table that summarizes the speaker characteristics most relevant for human-computer and user-centered applications. It also includes references to studies or implementation examples.

thereof. This section does not cover all the applications mentioned.

They are also described in great detail elsewhere in this issue. These include PD consumer email database

53 Speaker Characteristics

Forensic applications where the characteristics gender, age and medical conditions are known.

Language, accent, and sociolect all play an important role. Jessen provides a comprehensive overview of forensic applications in this issue [37]. We did not also discuss Purchasing Department Email List

Emerging applications for home parole, detection and fraud in the

context of Law Enforcement that are concerned with the speaker’s identity

emotion. This article provides an introduction to the field regarding emotion. buy PD targeted email list

Eriksson, in this issue [38].

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Reference to Characteristic Applications

Identity Transaction Authentication [39] ; Access Control [8]

Dialog Systems [14]; Meeting Browser [25] PD business database

Gender Dialog Systems [32]; Speech Synthesis ([3]

Forensics [37]

age Dialog Systems [32]; Forensics [37]

Speech Synthesis [19]

health Forensics [37]

Language Call Routing [15] ; Speech Translation [30]

dialect Forensics [37]

Accent Language Learning [21]; Dialog Systems

Speech Synthesis [19] ; Forensics[37]

Assessment Systems [20]

sociolect Forensics [37]

idiolect Speaker Recognition [35]; Forensics [37]

emotional state Translation [40]; Meeting browser [25]

Law Enforcement [38]; Dialog Systems (18,17]

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Attentional state Human-Robot Interaction [41] Smart Workspaces (26,23,24] Purchasing Department Email List

Relationship/role Translation [31]

Cultural background Dialog Systems [22]

3 What? 3 What?

These are the discrete speaker classes to which vectors for speech features are assigned.

A speaker’s characteristics. These characteristics are relevant for speech-based applications. We have created a hierarchical structure.

As described above.

Figure 1 illustrates the proposal taxonomy. It distinguishes first and foremost between psychological and physiological aspects of speaker characteristics. The

These aspects are further sub-divisioned into those that concern each speaker

Contrary to those that are specific to a community or group. For

A speaker could be a professor at university, a wife or mother to her children, or both. The authority of a PD consumer email database

The context in which the speaker speaks may be different. The hierarchy is determined by the people he/she talks to. Credibility may also depend on the person being done. Purchasing Department Email List

54 T. Schultz

Speaker Characteristics

Psychological

Individual collective identity, gender health, age

Geographic

Background

Attention

State

Emotional

State

Relationship role

language accent dialect

idiolect sociolect

Fig. 1. Taxonomy of Speaker Characteristics

business with, etc. The definition of the group “collective” is required.

A relationship between the sender and the receiver.

This taxonomy has its limitations. It does not include all subjects.

Aspects of an individual (e.g. Weight, height, smoking and drinking habits, demographics like race, income mobility, employment status, or other special aspects

Speech pathologies are an example of this, but instead we should be focusing on the characteristics that we think are important.

To be applicable (and assessable in the context typical speech applications. buy PD targeted email list

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The taxonomy also does not indicate which levels of linguistic information are required to distinguish between different characteristics. This is an example: PD business database

Low-level acoustic features can usually be used to distinguish gender; phonetic, however, is often sufficient.

To discriminate between idiolects, phonological and lexical knowledge may be necessary.

It needs syntactic and semantic information to distinguish sociolects.

To understand the role of speakers, and their role in society, pragmatics may be required. Purchasing Department Email List

Relationship to a group. Low-level physical aspects can be relatively simple

High level cues, which are hard to extract automatically, can be difficult to assess. This is a result.

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Most automatic systems for speaker recognition are still focused on low-level speakers.

cues.

The taxonomy does not include discrimination.

between stable versus transient characteristics. Speaker identity and gender are examples of stable characteristics. Transient characteristics can change over time

time. This aspect could be important for practical applications, particularly if it is a characteristic that underlies dynamic changes over the course of a single period.

audio recording session. Although there are locally stable characteristics like age and health, PD consumer email database

Language, accent, dialect, and even idiolect can change much more slowly than the others. Purchasing Department Email List

Duration of the recording session, characteristics like attentional and emotional

55 Speaker Characteristics

The state of the speaker and the context or topic changing dynamically. The

Over the course of an interaction, the relationship between a speaker and the listener can change. The collective may also have other characteristics, such as sociolect.

The spoken language has many functions, including dialect, accent, and dialect.

It is quite stable within the same language. If a speaker changes languages during a recording session, then the class assignments for accent, idiolect and idiolect will be changed.

Both the dialect and English language can often change. PD email database free

3.1 Language-Dependent Speaker Characteristics

The following outlines the five characteristics language accent dialect.

Language-dependent speaker characteristics include sociolect and idiolect. They are somewhat dependent upon the language used by the speaker.

speaker.

The line between genuinely different languages and dialects of English is drawn

The same language can be the subject of many disputes. We define a dialect as a regional

Modifications at the lexical or grammatical levels are a variant of a language. Accent, on the other hand, is a regional variant that affects only pronunciation. It mainly affects phonetic realizations, but also prosody and allophonic distribution.

Fluency and grammar. For example, British Received Pronunciation is an accent on English

Scottish English, on the other hand, would be considered a dialect because it frequently exhibits

Grammatical differences such as “Are you no going?” or “Aren’t you going?” Purchasing Department Email List

(see [42]). It is assumed that dialects of the same language can be understood by each other. buy PD targeted email list

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Different languages may not be the same, but they are different. Languages need to be learned explicitly, even though they may not be the same.

Language speakers from other languages. Languages also have their own literary characteristics. PD business database

Tradition, dialects are primarily spoken varieties of languages without any literary tradition.

These definitions have been greatly simplified. Many languages do not have a written system, and therefore lack any literary tradition. The distinction between

Languages and dialects are a continuum, not a binary choice. They are often motivated by sociopolitical rather that linguistic considerations. Chinese

Languages, for instance, are unified by a common writing system. However they have their own distinct syntax. PD email database free

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a large number of unintelligible and mutually incompatible varieties, which differ significantly in grammar, vocabulary, pronunciation, and grammar. Most linguists will argue that

These variations can be considered different languages and are officially called dialects.

Encourage the idea of Chinese national unity (see “42”)). The exact opposite

This happened for Serbo­Croatian (the official language of the former Yugoslavia). After

After the split, the languages of Croatian and Serbian were referred to as

Separate languages to highlight national independence Purchasing Department Email List

Languages exhibit sociolectal and idiolectal variations in addition to regional variations.

variation. An idiolect is a speech pattern that has a consistent pronunciation, lexical choice or grammar and which is specific to one speaker. Some idiolectal patterns can include speaker-specific repetitive phrases (e.g. a tendency

To start sentences with Well, to put it simply …), characteristic intonation pattern,

or divergent pronunciations (e.g. nucular instead of nuclear (see [42]). A sociolect is a collection of variations that are typical of a defined group of speakers.

Not by regional cohesion, but by social parameters like economic status and age PD email database free

56 T. Schultz

profession, etc. Some dialects can be considered both a sociolect and a dialect, as they often reflect a specific social status. You can take, for example:

Standard German is very similar to dialects spoken in Hannover or the

state of Saxony–Anhalt, which is the source of Martin Luther’s bible

Translation was the foundation for standard German development. Thus,

Standard German, while being a dialect in these specific areas, is also a sociolect in the sense that it carries a certain prestige as the national language.

Germany is used in all aspects of broadcasting, press and by citizens throughout the country.

higher education.

Despite all the efforts made to make speech recognition systems more robust for real-world applications, regional variations remain a major problem.

challenge. Non-native words can lead to significant increases in word error rates buy PD database for marketing

[43,44] & dialectal speech [45]. This performance is a result of one thing. Purchasing Department Email List

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Degradation is when acoustic models are customized and pronunciation dictionaries are targeted

towards native speakers and lacks the variety that comes from non-native pronunciations. The lexicon as well as the language model do not include dialectal variety. The PD business database

The straightforward solution to deploying accent- or dialect-specific speech recognizers is not possible due to two limitations: a lack of platform resources, and a lack of infrastructure.

Data. Mobile or automotive applications are particularly embedded and restrict the integration of multiple recognizers in one system. Even if

Resources permit the deployment dialect- or accent-specific systems. PD email database free

This results in very limited data resources. Real-world applications therefore require cross-dialect recognition or non-native recognition. Refer to the reader

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For a complete introduction to this area, see [36]. You can find idiolectal features here.

This is used to tailor a speech application for a particular user, such as in training

A speech-based, automated office assistant. Additionally, the software includes idiolectal capabilities.

[35] These features have been proven to be useful in automatic speaker identification. When developing an application, it is also possible to consider sociolectal characteristics. Purchasing Department Email List

an entire user group.

Individual

Accent

Phonetic Lexical

Grammar

k idiolect: l

Language

Collective

k sociolect l

Language k

Fig. 2. Language-dependent Characteristics PD business email database free download

Speaker Characteristics 57

Multilingual environments may have an impact on idiolectal or sociolectal variations. For example, [46] has evidence that bilingual speakers alter their L1 speech after they speak in multilingual settings.

Spending time in a L2-speaking environment. There are many techniques that can improve your speech

Recognition performance in the presence code-switching has been studied [47,48]. The act of using words and phrases from code-switching is known as code-switching.

Multilingual speakers often use different languages in the same sentence.

Engaged in informal conversations

Figure 2 shows the similarities and differences between the languagedependent characteristics language dialect accent, sociolect, idiolect and accent. Main

The effects of linguistic aspects on discriminating factors and whether they are relevant to the individual, are considered discriminating factors.

Individuals and groups can have the same characteristics. Purchasing Department Email List

4 How? 4 How?

Characteristics PD business email database free download

The most prominent and widely studied task in investigating the is probably the.

“Assignment speech features to discrete speakers classes” is speaker recognition

(Who is speaking, class=identity) & language identification (which language).

spoken, class=language). Speech recognition (what is being said, class=content) addresses

This is a larger problem that could be considered part of the “Speaker Classification”

When high-level characteristics such as topic, content, or role are being investigated. As it has become apparent that there are solutions to these three tasks, they have grown closer.

One task might be more beneficial than the other and all three must be done together.

Study of speech-based real-world applications to improve speech quality The following are the results.

We will briefly discuss speaker recognition and language identification. This section buy PD database for marketing

It is not intended to be a complete introduction. For more information, the reader should refer to

Refer to in-depth overviews such as [49] language identification and [39.50] for language classification.

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For speaker recognition. This article is a good introduction to speech recognition. PD customers database

In [51].

4.1 Speaker Recognition

The level of linguistic understanding applied to solving the classification task can help distinguish between different classification approaches. Reynolds identifies a hierarchy perceptual cues humans use to recognize speakers.

[39]. At the highest level, people use semantics and diction.

These are ideosynchrasies that arise from socio-economic status and education.

the place of birth of the speaker. Prosodic features are located on the second level.

The characteristics of personality and parental influence are rhythm, speed and intonation. People are at the lowest level of linguistic communication.

Use the acoustic aspect of sounds such as nasality and breathiness to determine their authenticity. PD business email database free download

Let’s draw some conclusions about the anatomical structure and vocal range of the speaker.

apparatus. Low level physical cues can be extracted automatically. However, it is difficult to determine high-level cues. Most automatic systems are therefore able to detect high-level cues. Purchasing Department Email List

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Systems for speaker recognition still focus on low-level cues.

58 T. Schultz

Conventional systems use Gaussian Mixture Models to capture

frame-level characteristics [52]. GMMs are often unable to distinguish speaker-specific information that changes over multiple frames, as speech frames are presumed to be independent of each other. GMMs do not perform well.

These are able to discriminate speakers based upon higher-level differences such as idiolect. GMMs can also be affected by mismatched acoustics

Conditions that rely solely on low-level speech signal features. To overcome

These problems have led to speaker recognition focusing on higher-level linguistic features such as phonetic information emerging out of speaker ideosynchrasies [35]. This is known as phonetic speaker recognition. It applies relative

Frequency from phone ngrams [53]. This method is being intensively researched [39] and further developed by various modeling strategies, variations in statistical

n-gram models [54], various classifiers such as Support Vector Machines [55], PD business email database free download

Modeling cross-stream dimensions to uncover underlying phone dependencies across multiple language [54,56].

4.2 Language Identification

Language identification methods can be classified in the same way as speaker recognition. This classification is based on the level of linguistic data.

task. [49] distinguishes the signal processing level, and the unit level (e.g. phones),

The sentence level is the word level. These levels allow him to distinguish between spectral features-based acoustic approaches and language identification. Purchasing Department Email List

derived from speech segments [57], Phonotactic approaches that use the contraints relative frequencies of sound unit [58], and other derivatives

Multilingual phone recognizers used as tokenizers [59], extended N-grams [60],

Cross-stream modeling [61], as well as combinations of GMMs, phonotactic and phonotactic model [62]. Navr’atil [49] also lists prosodic approaches that use tone

Intuition, prominence, and prominence [63], as well as those approaches that use full speech buy PD database for marketing

Recognition of language identification [64]

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5 A Classification System For Speaker Characteristics

This section presents a general classification system that applies one framework to the classification and classification of different speaker characteristics. PD customers database

Identity, gender, accent, proficiency, level of attention, and language

speaker. Framework uses high-level phonetic information in order to capture speakers’ ideasynchrasies. This was originally proposed by [58] within the context of language.

Identification and [35] in context of speaker recognition. The idea behind the basic concept is to

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To decode speech using various phone recognizers, and to use the relative frequency of

Phone n-grams are features for training speaker characteristics models and their PD business email database free download

classification. By using more language-independent languages, we enrich existing algorithms with the application of different speaker characteristics.

Phone recognizers and modeling dependencies across multiple phone channels Purchasing Department Email List

[54]. We also investigate the impact of different decision rules and examine their implications.

Speaker Characteristics 59

Consider the number of languages involved and compare multilingual to multi-engineering.

Approaches with respect to classification performance

5.1 Multilingual Phone Sequences

Our experiments were done using the GlobalPhone phone recognition software

Project [65] is available in 12 languages Arabic (AR), Mandarin Chinese(CH),

Croatian (KR), German, French (DE), Japanese (JA), Korean [KO], Portuguese (PO), Russian (“RU”), Spanish (SP), Swedish “SW”) and Turkish (“TU”)

These phone recognizers were trained with the Janus Speech Recognition

Toolkit. The acoustic model is composed of a context-independent, 3-state HMM

16 Gaussians per State in the system. The 13 Mel-scale is the basis of the Gaussians

Cepstral power and coefficients, with first- and second-order derivatives. These are the following

Cepstral subtraction, Linear Discriminant Analysis reduces the input vector PD email database free download

Up to 16 dimensions. Vocal tract length normalization (VTLN), is a part of training.

Normalization of speaker sounds To decode, unsupervised MLLR is used to locate the

Best matching warp factor to the test speaker. The decoding process is done with

Viterbi searches using a fully connected network of monophones null-grammar networks

i.e. The recognition process does not require any prior knowledge of phone statistics.

Figure 3 illustrates the relationship between phone unit count and phone error.

Rates for ten languages Purchasing Department Email List

A language dependent phonetic model (n-gram) is created to train a model for a specific speaker characteristic. It is based on available training data.

We train phonetic bigram models based on the CMUCambridge Statistical language model toolkit [19]. Phonetic bigram models can be directly estimated using the data and not by applying universal background models, or adapting with background models. No transcriptions

Speech data is required for any stage of model training. Figure 4 illustrates the

Procedure for training a speaker identity model speaker k. buy PD database for marketing

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Phone recognizers (PR1 ,…,PRm), decode speaker k’s training data to create m phone strings. These phone strings are used to create m phonetic bigram models. PD customers database

(PM1,k ,…,PMm) are estimates for speaker k.

needs to be classified as one of the following: n-class speaker characteristic, m phone

Recognition software will produce models of m x 10 phonetic bigrams.

Each of the PRi m-phone recognizers, which are used to train phonetic bigram models, decodes the audio segment. Each of the resulting M phone strings is scored against one of the n bigram model PMi,j. This

This results in a perplexity matrix PP. The PPij element of the phonetic bigram model PMi on the phone string output is phone

Recognizer PRi We will investigate other options in future experiments.

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Our default decision algorithm is C* to suggest a class estimate

Select the j

Lowest

i(PP)i,j . This procedure is illustrated in Figure 5. We refer to it as MPM-pp.

The MPM-pp classification method is used in the following.

There are many classification tasks that can be done in relation to speaker characteristics.

Language classification PD email database free download

This section applies the MPM-pp framework for the multiclassification problem in four languages: Japanese, Russian (RU), Spanish and Spanish (SP).

Turkish (TU) We used a limited number of phone recognitions in

Languages other than the four languages of classification are used to duplicate the Purchasing Department Email List

Common circumstances for our identification experiments and to show a

Language independence that is maintained even when the language is identified

65 Speaker Characteristics

Table 8. Confusion matrix for 3-way proficiency classification using 6 versus 7 phone

Recognizers

Phonetic 6 languages 7 langues

model C-1 C-2 C-3 C-1 C-2 C-3

C-1 8 3 19 8 5 17

C-2 8 41 61 6 53 51

C-3 2 12 99 1 20 92

domain. Phone recognizers in Chinese, German, and French

With phone vocabulary sizes of 145 to 47 and 42 respectively, these were borrowed from

The GlobalPhone project. Data for this experiment were also available. PD email database free download

It was borrowed from GlobalPhone but not used for training phone recognizers. It was broken up as shown in Table 9 Training was done with data set 1.

The phonetic models were used, and data set 4 was not available during training.

It is used to assess the performance of the entire classifier. Data

Sets 2 and 3 were used to experiment with different materials.

Decision strategies

Table 9. Table 9.

Set JA RU SP TU

nspk 1-20 20 20 20/20 20

2 5 10 9 10

3 3 5 5 5

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nutt all 2294 4923 2724 2924

tutt all 6 hrs 9 hrs 8 hrs 7 hrs

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To train the phonetic bigram models, you will need to utterances starting from set 1 in each Lj

Each of the three phone recognition algorithms PRi was used to decode JA, RO, SP, and TU

CH, DE, FR. Twelve trigram models with Kneser/Ney were created PD b2c database

No cut-off or backoff. The size of the training corpora varied from 140K to 500,000. PD email database free download

250K tokens. All 12 models had trigram coverage of 73% to 95%.

Unigram coverage less than 1%

Our lowest average perplexity decision was the first to benchmark accuracy.

rule. We constructed a 4-class multi-classifier using the same principles.

Data set 2 was for each of the durations tk: 5s, 10s and 20s respectively; data set 3 was

Cross-validation

Multi-classifier combines the outputs of multiple binary classifiers. Purchasing Department Email List

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ECOC is error-correcting output coding. A class space that contains 4 languages produces 7 binary partitions. Each of these was taught an independent instructor.

Multilayer Perceptron (MLP), 12 input units, 1 output unit that uses scaled

Conjugate gradients for data set 2 and early stopping with cross-validation

Crossover

Passive crossover networks are built using capacitors, inductors, or both.

Resistors are used to split the audio signal for high-frequency information

goes to the low-frequency information and high-frequency driver PD b2b database

The woofer is the winner. Passive crossovers offer the best value for money

Allow the amplifier to power the loudspeaker

channel. Passive crossovers work in most cases but are not recommended.

They are less precise than active crossovers.

To operate, active crossovers need external power. They can be powered by external power.

The output of a mixer or preamplifier is connected to the output of an amplifier.

Power amplifier. Active crossover has the advantage of being able to control power amplifiers.

You can change many characteristics, such as the crossover frequency.

The rate of transition.

A active crossover is usually an additional component you will need

To purchase. Check that your loudspeaker system is certified.

Separate inputs are needed to accommodate multiple amplifiers.

Your crossover. An especially cost-effective method to obtain an

Active crossover can be achieved by purchasing a Digital Signal Processor, (DSP).

You can create equalizers and crossovers.

Woofer

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The signal is sent through a loudspeaker…or more.

The signal chain contains the audio signal.

Use an instrument or microphone to communicate your message. Purchasing Department Email List

The Signal Chain

A sound system is a system that transmits an audio signal from its source to the system components. These components balance, process and amplify the signal.

A loudspeaker, or headphones, is released. The signal chain is the audio signal’s path.

Some signal chains can be complex with many components and divergences. Others are simple. Below is a diagram of a signal chain.

Basic, stripped-down signal chains

What Does an Audio System Look Like?

(c) 2012 Bosch Security Systems 10.

What Signal Voltage is and why it matters PD database for sale

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Signal voltage refers to the value of the signal being sent to an audio device. We usually refer to the value in decibels volts (dBV).

Each microphone, amplifier, pickup, and source of program has a unique voltage level. However, the signals cannot be mixed until they are all combined. PD b2c database

They are all promoted to “line” professional status.

Mic level is the relatively

Low-level signal (generally)

From -40 to -60 dBV (of a)

Microphone that must be

Amplified to line-level,

It is easier to manipulate

A mixing console.

 

Different levels of signals

Enter a mixer, but make sure the signal is clear

Professionals can take your leaves.

line level. The standard is

Audio: +4 dBu audio or -10 dBV

Levels, or about 1V.

We need stronger signals

to drive loudspeakers.

Amplifiers boost processed

Signals to the speaker level Purchasing Department Email List

So the loudspeakers are rewarded

There is enough power to get the job done.

(c) 2012 Bosch Security Systems

11

Coverage Pattern is a directional pattern for a loudspeaker system that can be varied by frequency or tone. One example is a loudspeaker’s

One output may cover a large and narrow area of a room while another might cover an equally long and tall section. PD b2b database

Pro Tip:

Make sure your loudspeakers cover your entire audience. People who are unable to hear you clearly are more likely not to be able to hear you.

Throw things on the stage.

Attachment: A cabinet that houses loudspeaker drivers as well as associated electronic hardware such crossover circuits. An

An enclosure can be as simple as a wooden box, or it could be complex and contain ports, baffles and acoustic insulation. The enclosure

This prevents sound waves from interfering between the rear and front, reduces vibration and heat generation.

Driver coils. It also shapes the low frequency response. To do this, the enclosure design must be compatible with the driver characteristics.

This was achieved with great success. Electro-Voice was the first company to implement the concept of matching enclosure designs systematically.

Drivers should be able to extend the low end as much as possible. Purchasing Department Email List

Frequency Response is a measurement of how well a speaker or electronic component reproduces sounds. Frequency: A frequency

Response specification refers to the frequency range and deviation from a perfectly flat response. PD b2b database

Frequency response curves can be used to show the accuracy of sound systems.

Frequency Range is the lower and higher limits of the system’s output. It is not possible for a loudspeaker to reproduce the sound of another speaker.

Anything below or over its rated frequency range is unacceptable, Buddy.

Pro Tip:

Different speaker manufacturers may use different standards. To make an accurate comparison, be sure to read the manufacturer’s footnotes.

The frequency range of a frequency measured at the -10 dB points will usually be larger than that measured at the 3 dB points. However, it is not fair.

Considerations when choosing a loudspeaker

Here are some helpful terms

You’ll be able to explore your options for loudspeakers and you’ll see many fancy terms such as frequency, impedance, and frequency.

Response, power rating, SPL. They are, however, very important. Here’s a quick primer.

Bosch Security Systems 12 (c) 2012 Bosch Security Systems

Impedance (Z), The resistance that an electronic circuit or device offers to the AC current flowing through it

It. It is often represented by the mathematical symbol Z and measured in Ohms. Impedance Purchasing Department Email List

This article describes the difficulty of a speaker to drive and its compatibility for various amplifiers. PD database for sale

This policy consists of allowing the update operation of the tuple and in
perform compensating operations that set null values to attributes
of the foreign key of the tuples that refer to it; this action is carried out
to maintain referential integrity.
Since relational DBMS generally allow establishing that a
certain attribute of a relation does not allow null values, it can only be
apply the override policy if the foreign key attributes do ad-
miten. CPO mailing lists
Override application example
The best way to understand what annulment is is through an example. We have
the following relationships:
• SELLERS relationship:
• CUSTOMER relationship:
* {vendedorasig} reference SELLERS.
More specifically, cancellation in case of deletion consists of per-
allow the deletion of a tuple t that has a referenced key and, in addition,
else, modify all tuples that reference t, so that the
attributes of the corresponding foreign key take null values.
Similarly, cancellation in case of modification consists of
allow modification of attributes of the primary key of a tuple

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t that has a referenced key and additionally modify all tuples
referencing t, so that the attributes of the corresponding foreign key
slope take null values.
 FUOC • 71Z799014MO 30 The relational model and relational algebra
a) If we apply the annulment in case of deletion and, for example, we want to delete the seller
number 1, all the clients that had it assigned will be modified, and they will have a value
null lor in sellersig. We will have:
• SELLERS relationship:
• CUSTOMER relationship:
* {vendedorasig} reference SELLERS.
b) If we apply the override on modification, and now we want to change the number
of the seller 2 by 5, all the clients that had it assigned will be modified and will pass to
have a null value in sellersig. We will have:
• SELLERS relationship:
• CUSTOMER relationship:
* {vendedorasig} reference SELLERS.
4.3.4. Selection of the maintenance policy
of referential integrity email marketing database CPO
We have seen that in case of deletion or modification of a primary key,
differentiated by some foreign key, there are several key maintenance policies.
the referential integrity rule.

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The way to define these policies of
maintaining integrity with
the SQL language is explained in the unit
“The SQL language” of this course.
 FUOC • 71Z799014MO 31 The relational model and relational algebra CPO address lists
The designer can choose for each foreign key which policy will be applied in
case of deletion of the referenced primary key, and which in case of modification
tion of it. The designer must take into account the meaning of each key
concrete foreign to be able to choose appropriately.
4.4. Domain Integrity Rule
The domain integrity rule is related, as its name suggests,
with the notion of domain. This rule establishes two conditions.

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This condition implies that all non-null values contained in the base of
data for a given attribute must be from the domain declared for di-
cho attribute.
Example
If in the relation EMPLOYEES (DNI, name, surname, ageemp) we have declared that domi-
nio(DNI) is the predefined domain of integers, so we will not be able to insert, for example,
For example, no employee whose DNI has the value “Luis”, which is not an integer.
Let us remember that domains can be of two types: predefined or defined.
two per user. Note that user-defined domains result in
so very useful, because they allow us to determine more specifically
what will be the values admitted by the attributes.
Example
Suppose now that in the relation EMPLOYEES(DNI, name, surname, ageemp) we have
declared that domain(empage) is the domain defined by the user age. suppose
also that the age domain has been defined as the set of integers between
16 and 65. In this case, for example, it will not be possible to insert an employee with a value of 90
for ageemp.
The second condition of the domain integrity rule is more complex,
especially in the case of user-defined domains; the DBMS ac-
Current ones do not support it for these last domains. For these reasons only the
We will present superficially.
The first condition is that a non-null value of an attribute
A i must belong to the domain of the attribute A i; that is, it must belong
to domain(Ai).
This second condition serves to establish that the operators that

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can be applied to the values depend on the domains of these
values; that is, a given operator can only be applied on
values that have domains that are suitable for it.
Policy Enforcement CPO mailing lists
different
It may happen that for a
certain foreign key, the
appropriate policy in case
erase is different from the
suitable in case of modification
tion. For example, it can be
need to apply the restriction
in case of deletion and update
cascading in case
of modification.
Remember that the concepts
predefined domain and domain
user-defined have been explained
in subsection 2.2 of this unit
didactic
further reading
To study in more detail
the second condition
of the integrity rule
of domain, you can consult
the following work:
CJ Date (2001).
Introduction to systems
of databases (7th ed.,
chap. 19). Prentice Hall.
 FUOC • 71Z799014MO 32 The relational model and relational algebra
Example
We will analyze this second condition of the domain integrity rule with an example
concrete. If in the relation EMPLOYEES (DNI, name, surname, ageemp) it has been declared that
domain(DNI) is the predefined domain of integers, so it will not be allowed to query
all those employees whose DNI is equal to ‘Elena’ (DNI = ‘Elena’). The reason is not
it makes sense that the comparison operator = be applied between a DNI that has for domain
nio the integers, and the value ‘Elena’, which is a character string.
Thus, the fact that the operators that can be applied to the email marketing database CPO
values depend on the domain of these values allows to detect errors that are
they might commit when the database is queried or updated. The domi-
User-defined definitions are very useful, because they will allow us to determine
specify more specifically which operators can be applied
about values.

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Example
Let’s look at another example with user-defined domains. Suppose that in the knowledge
gives relationship EMPLOYEES (DNI, name, surname, ageemp) it has been declared that domain (DNI)
is the domain defined by the user IDnumbers and that domain(ageemp) is the domain of-
user-defined age. Suppose that DNInumbers corresponds to the positive integers CPO database for sale
and what age corresponds to the integers that are between 16 and 65. In this case, it will be incorrect,
for example, query the employees who have the DNI value equal to the empage value.
The reason is that, although both the DNI and empage values are integers, their do-
minions are different; therefore, according to the meaning that the user gives them, it does not make sense
compare them.

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However, current relational DBMSs do not support the second con-
addition of the domain integrity rule for domains defined by the
Username. If you wanted to do it, it would be necessary for the designer to have some
way of specifying, for each operator that one wanted to use, for what
User-defined domain joins make sense to apply.
The SQL standard language does not currently include this possibility.
 FUOC • 71Z799014MO 33 The relational model and relational algebra
5. The relational algebra
As we have already commented in the section dedicated to the operations of the
relational, relational algebra is inspired by set theory to
specify queries in a relational database.
To specify a query in relational algebra, you must define one or more
more steps that serve to build, through algebra operations
relational, a new relation that contains the data that responds to the relation
results from the stored relationships. Languages based on the alge-
relational code are procedural, since the steps that make up the query
describe a procedure.
The vision that we will present is that of a theoretical language and, therefore, we will include-
We only describe its fundamental operations, and not the constructions that could be
add to a commercial language to facilitate issues such as the or-
presentation of the result, the calculation of aggregate data, etc.
Relational algebra operations have been classified according to different criteria. CPO quality email lists
theria; of all of them we indicate the following three:
1) Depending on whether or not they can be expressed in terms of other operations.
a) Primitive operations: are those operations from which we can
Let’s define the rest. These operations are union, difference, product.
Cartesian to, selection and projection.
b) Non-primitive operations: the rest of the operations of the relational algebra
that are not strictly necessary, because they can be expressed in terms of
we of the primitives; however, non-primitive operations allow

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formulate some queries more comfortably. There are different versions
relational algebra, depending on the non-primitive operations included. Nope-
We will study the non-primitive operations that are used most frequently.
sequence: the intersection and the combination.
2) According to the relationship number ones that have as operands:
a) Binary operations: they are those that have two relations as operands.
All operations except selection and projection are binary.
A remarkable feature of all the operations of relative algebra
The rationale is that both the operands and the result are relations. Is
property is called a relational closure.
Consult section 3
of this teaching unit.
Closure Implications
relational
The fact that the result
of an algebra operation
relational be a new
relationship has implications
important:
1. The result of an operation
tion can act as
operand of another operation.
2. The result of a email marketing database CPO
operation will meet all
the features that already
We know about the relationships:
non-ordering of tuples,
absence of repeated tuples,
etc.

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 FUOC • 71Z799014MO 34 The relational model and relational algebra
b) Unary operations: they are those that have a single relation as operand.
do. Selection and projection are unary.
3) According to whether or not they resemble the operations of set theory:
a) Set operations: they are those that resemble those of the theory of CPO database for sale
sets. These are the union, intersection, difference, and product.
Cartesian.
b) Specifically relational operations: they are the rest of the operations;
that is, selection, projection, and combination.
As we have already mentioned, the relational algebra operations
result in a new relationship.

 

That is, if we do an operation
tion of algebra such as EMPLOYEES_ADM ∪ ∪ EMPLOYEES_PROD
to obtain the union of the relations EMPLOYEES_ADM and EMPLOYEES_PROD,
the result of the operation is a new relation that has the union of the tuples
of the starting relationships.
This new relationship must have a name. In principle, we consider that your
name is the same relational algebra expression that gets it; namely,
the same expression EMPLOYEES_ADM ∪ EMPLOYEES_PROD. Since this CPO quality email lists
name is long, sometimes it can be interesting to change it for one more
simple. This will make it easier for us to refer to the new relationship, and it will be especially
mind useful in cases where we want to use it as an operand of another
operation. We will use the helper operation rename for this purpose.
In the example, to give the name EMPLOYEES to the relation resulting from the
operation EMPLOYEES_ADM ∪ EMPLOYEES_PROD, we would do:
EMPLOYEES := EMPLOYEES_ADM ∪ EMPLOYEES_PROD.
Each relational algebra operation gives default names to the attributes.
Butos of the scheme of the resulting relationship, as we will see later.
In some cases, it may be necessary to change these default names to
other names. For this reason, we will also allow renaming
the relation and its attributes using the rename operation.
The rename operation, which we will denote with the symbol :=, allows
assign a name R to the relation that results from an operation of the
relational algebra; it does it as follows:
R := E,
where E is the expression of a relational algebra operation.
algebra operations
relational classified according to
whether they are ensemble or specifically
relationships are studied in
subsections 5.1 and 5.2 of this unit.
 FUOC • 71Z799014MO 35 The relational model and relational algebra
Here is an example that we will use to illustrate the
relational algebra operations. Later we will see in detail the operations
tions.
Suppose we have a relational database with the four relations
following tions:
1) The relationship BUILDINGS_EMP, which contains data from different buildings of the
that a company has to carry out its activities.
2) The DESPACHOS relation, which contains data on each of the dispatches
that is in the previous buildings.
3) The relation EMPLOYEES_ADM, which contains the data of the employees of
the company that carry out administrative tasks.
4) The relation EMPLOYEES_PROD, which stores the data of the employees CPO consumer email database
of the company dealing with production tasks.
Next we describe the schemes of the previous relations and their ex-
stresses at a given time:

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Cartesian product
If we calculate the Cartesian product of BUILDINGS_EMP and OFFICES, we will obtain a
new relation containing all possible concatenations of EMP_BUILDINGS tuples
with DISPATCH tuples.
If you want to calculate the Cartesian product of two relations that have some CPO database for sale
common attribute name, it is only necessary to previously rename the attributes
suitable butos of one of the two relationships.
Next we define the attributes and the extension of the resulting relation
of a Cartesian product.
Cartesian product example
The Cartesian product of the relations OFFICES and BUILDINGS_EMP in the example can be
do as indicated (it is necessary to rename attributes previously):
BUILDINGS(buildingname, supmediadesp) := BUILDINGS_EMP(building, supmediadesp).
R := BUILDINGS × OFFICES.
Then, the resulting relation R will be:
The Cartesian product is an operation that, from two relations,
obtains a new relation formed by all the tuples that result
of concatenating tuples from the first relation with tuples from the second.
The Cartesian product is a binary operation. Being T and S two rela-
tions that satisfy that their schemas do not have any name of
common attribute, the Cartesian product of T and S is denoted as T × S.
The schema attributes of the resulting relation of T × S are all
the attributes of T and all the attributes of S*.
The extension of the resulting relation of T × S is the set of all
the tuples of the form <v1, v2, …, v n , w1, w2, …, w m> for which
ple that <v1, v2, …, v n> belongs to the extension of T and that <w1, w2, …,
w m> belongs to the extension of S.
R
buildingname supmediadesp building number area
Navy 15 Navy 120 10 CPO quality email lists
Navy 15 Navy 230 20
Marine 15 Diagonal 120 10
Marina 15 Diagonal 440 10
Diagonal 10 Navy 120 10
* Remember that T and S do not have
no common attribute name.
 FUOC • 71Z799014MO 41 The relational model and relational algebra
It should be noted that the Cartesian product is an operation that is rarely
is used explicitly, because the result it gives is usually not useful for
resolve common queries.
Despite this, the Cartesian product is included in the relational algebra because
which is a primitive operation; from which another operation of the
algebra, the combination, which is used very often.
5.2. Specifically relational operations
The specifically relational operations are selection, projection
and the combination.
5.2.1. Selection
To obtain a relationship that has all the offices of the Marina building that have more
of 12 square meters, we can apply a selection to the DESPACHOS relationship with a
selection condition that is building = Marina and area > 12; DESPA- would be indicated
CHOS(building = Marina and area > 12).
In general, the selection condition C is made up of one or more clauses
from the way:

buildingname supmediadesp building number area
Diagonal 10 Marina 230 20
Diagonal 10 Diagonal 120 10
Diagonal 10 Diagonal 440 10
We can see the selection as an operation that serves to choose some
some tuples from a relation and remove the rest. More specifically, the
selection is an operation that, from a relation, obtains a CPO consumer email database
new relation formed by all the tuples of the starting relation
that meet a specified selection condition.

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Selection is a unary operation. Being C a condition of se-
lesson, the selection of T with the condition C is denoted as T(C).
 FUOC • 71Z799014MO 42 The relational model and relational algebra
where Ai and Aj are attributes of the relation T, θ is a comparison operator*
and v is a value. Furthermore, it holds that: buy CPO database for marketing
• In clauses of the form Ai θ v, v is a value of the domain of Ai.
• In clauses of the form Ai, θ Aj , Ai and Aj have the same domain.
The clauses that form a selection condition are connected with the following:
following boolean operators: “and” (∧) and “or” (∨).
Next we define the attributes and the extension of the resulting relation
of a selection.
Selection Example
If we want to obtain a relation R with the dispatches of the database of the example that
are in the Marina building and that have an area of more than 12 square meters,
We will make the following selection:
R := OFFICES(building = Marina and area > 12).
The resulting relation R will be:
5.2.2. Projection
The schema attributes of the resulting relation of T(C) match
with the attributes of the schema of the relation T.
The extension of the resulting relation of T(C) is the set of tuples
that belong to the extension of T and that satisfy the condition of se-
lesson C. A tuple t satisfies a selection condition C if, after
substituting each attribute in C for its value in t, the condition C
evaluates to the true value.
Rbuilding number surface
Navy 230 20
We can consider the projection as an operation that serves to
choose some attributes of a relation and eliminate the rest. More concrete-
Simply put, projection is an operation that, starting from a relation,
obtains a new relation formed by all the (sub)tuples of the relation purchase CPO email lists
starting relationship that result from removing specified attributes.
* That is, =, ≠, <, ≤, >, or ≥.
 FUOC • 71Z799014MO 43 The relational model and relational algebra
To obtain a relationship that has only the first and last name attributes of the employees of
administration, we can make a projection in the relation EMPLOYEES_ADM on these
two attributes. It would be indicated as follows: EMPLOYEES_ADM [name, surname].
Next we will define the attributes and the extension of the resulting relationship.
much of a projection.
projection example
If we want to obtain a relation R with the name and surname of all the employees of
administration of the example database, we will make the following projection:
R := EMPLOYEES_ADM[first name, last name].
Then, the resulting relation R will be:
5.2.3. Combination
The projection is a unary operation. Being {Ai , Aj, …, A k} a subcon-
along with the attributes of the schema of the relation T, the projection of T
over {A i, Aj , …, Ak} is denoted as T[Ai , A j, …, Ak].
The schema attributes of the resulting relation of T[Ai, Aj, …, Ak]are the attributes {Ai, Aj, …, Ak}.
The extension of the relation resulting from T[Ai , Aj , …, Ak] is the set
to of all tuples of the form <t.Ai, t.A j, …, t.A k>, where it holds
that t is a tuple of the extension of T and where t.Ap denotes the value for
the Ap attribute of tuple t.
R
name last Name
John Garcia
Martha Rock
The combination is an operation that, starting from two relations, obtains
ne a new relation formed by all the tuples that result from con-
string tuples from the first relation with tuples from the second, and that
satisfy a specified join condition.
The join is a binary operation. Being T and S two relations CPO consumer email database
whose schemas have no common attribute name, and being
B a combination condition, the combination of T and S according to the con-
addition B is indicated T[B]S.
Elimination of tuples

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Note that the projection
implicitly remove all
the repeated tuples. The result-
do of a projection is a
valid relationship and cannot buy CPO database for marketing
have repetitions of tuples.
 FUOC • 71Z799014MO 44 The relational model and relational algebra
To get a relationship that has the data of each one of the administrative employees,
together with the data of the offices where they work, we can make a comparison purchase CPO email lists
combination of the relations EMPLOYEES_ADM and OFFICES, where the condition of
combination indicate the following: buildingdesp = building and numberdesp = number. The condition
The merge function causes the result to only merge the data of one employee with
the data of an office if the office building and the office number of the employee are equal to
building and office number, respectively. That is, the condition makes the
an employee’s data is combined with the data of the office where she works, but not
with data from other offices.
The combination of the previous example would be indicated as follows:
EMPLOYEES_ADM[buildingdesp = building, numberdesp = number] OFFICES.
If you want to combine two relations that have some attribute name co-
common, it is only necessary to previously rename the repeated attributes of a
Of the two.
In general, the condition B of a combination T[B]S is formed by one or
more comparisons of the form
Ai θ Aj ,
where A i is an attribute of the relation T, Aj is an attribute of the relation S, θ is
a comparison operator ( =, ≠, <, ≤, >, ≥), and it is true that Ai and Aj have
the same domain. Comparisons of a join condition are
separated by commas.
Next we define the attributes and the extension of the resulting relation
of a combination.
Combination Example
Let us suppose that we want to find the data of the offices that have a ma-
greater than or equal to the average area of the offices in the building where they are located. The IF-
The following combination will provide us with the data of these dispatches together with the data of
your building (note that the attributes must first be renamed):
BUILDINGS(buildingname,srmediasp) := BUILDINGS_EMP(building,srmediasp),
The schema attributes of the resulting relation of T[B]S are all
two the attributes of T and all the attributes of S*.
The extension of the resulting relation of T[B]S is the set of tu-
plas that belong to the extension of the Cartesian product T × S and that
satisfy all the comparisons that form the combination condition. purchase CPO email lists
nation B. A tuple t satisfies a comparison if, after substituting
each attribute that appears in the comparison by its value in t, the comparison
ration is evaluated to the true value.
* Remember that T and S do not have
no common attribute name.
 FUOC • 71Z799014MO 45 The relational model and relational algebra
R := BUILDINGS[buildingname = building, supmediadesp ≤ surface] OFFICES.
Then, the resulting relation R will be:
Suppose now that in order to obtain the data of each one of the administrative employees,
tion, together with the data of the office where they work, we use the following combination:
R := EMPLOYEES_ADM[buildingdesp = building, numberdesp = number] OFFICES.
The resulting relation R will be:
The relation R combines the data of each employee with the data of her office.
The combination is sometimes called a θ-combination, and when
all comparisons of the join condition have the operator
“=”, is called an equijoin.
According to this, the combination of the last example is an equijoin.
Note that the result of an equijoin always includes one or CPO email database free
more pairs of attributes that have identical values in all tuples.
In the example above, the values of buildingoff match those of building, and the values of
despnumber match those of number.
Since one of each pair of attributes is superfluous, a
combination variant called natural combination, in order to
remove them.

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Example of natural combination
If we do:
R := BUILDINGS_EMP * OFFICES,
R
buildingname supmediadesp building number area
Navy 15 Navy 230 20
Diagonal 10 Diagonal 120 10
Diagonal 10 Diagonal 440 10
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DNI name surname desp building desp number building surface area number
40,444,255 Juan Garcia Navy 120 Navy 120 10
33,567,711 Marta Roca Navy 120 Navy 120 10
The natural combination of two relations T and S is denoted as T * S
and basically consists of equijoining followed by elimination.
tion of superfluous attributes; In addition, it is considered by default that
the join condition matches all pairs of attributes that
they have the same name in T and in S.
Note that, unlike equicombination, natural combination
tural applies to relationships that have common attribute names.
 FUOC • 71Z799014MO 46 The relational model and relational algebra
the condition is considered to be building = building because building is the only attribute name
buto that appears both in the EDIFICIOS_EMP scheme and in the OFFICES scheme.
The result of this natural combination is:
Notice that one of the building name attributes has been removed.
Sometimes, before the natural combination it is necessary to apply the operation
rename function to match the names of the attributes that we in-
teresa match buy CPO targeted email list
Example of natural combination with redenomination
For example, if we want to obtain the data of each of the administration employees
together with the data of the office where they work but without repeating values of super-
fluos, we will make the following natural combination, which requires a previous redenomination:

DNI name surname building desp number desp area
40,444,255 Juan Garcia Marina 120 10
33,567,711 Marta Roca Marina 120 10
In many cases, to formulate a query in relational algebra it is
Several operations must be used, which are applied in a certain order.
To do so, there are two possibilities:
1) Use a single algebra expression that includes all the operations
tions with the necessary parentheses to indicate the order of application.
2) Decompose the expression into several steps where each step applies
a single operation and obtain an intermediate relationship that can be used
perform in the subsequent steps.
 FUOC • 71Z799014MO 47 The relational model and relational algebra
Example of using sequences of operations
To obtain the name and surname of the employees, both administrative and pro-
duction, it is necessary to make a union of EMPLOYEES_ADM and EMPLOYEES_PROD, and then
then make a projection on the first and last name attributes. The operation can be
express in the following ways: CPO email database free
a) A single expression can be used:
R := (EMPLOYEES_ADM ∪ EMPLOYEES_PROD) [first name, last name].
b) Or we can express it in two steps:
• EMPS := EMPLOYEES_ADM ∪ EMPLOYEES_PROD;
• R := EMPS[first name, last name]

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In cases where a query requires many operations to be performed, the result
The second alternative is simpler, because it avoids complex expressions.
Other examples of queries formulated with sequences of operations
Let’s look at some examples of database queries formulated with sequences of operations.
relational algebra rations. buy CPO targeted email list
1) To get the building name and house number offices located in buildings in
where the average area of these offices is greater than 12, we can use the following
sequence of operations:
• A := BUILDINGS_EMP(supmediaoff > 12);
• B := OFFICES * A;
• R := B[building, number]2) Suppose now that you want to get the first and last name of all employees
(both administration and production) who are assigned to office 120 of the building
Marine job. In this case, we can use the following sequence:
• A := EMPLOYEES_ADM ∪ EMPLOYEES_PROD;
• B := A(buildingdesp = Marina and numberdesp = 120);
• R := B[first name, last name].
3) If we want to consult the name of the building and the number of the offices that no
admin employee is assigned, we can use this sequence:
• A := OFFICES [building, number];
• B := EMPLOYEES_ADM[despbuilding,despnumber];
• R := A – B.
4) To obtain the DNI, the name and surname of all the administration employees who
have office, together with the surface of your office, we can do the following:
• A[DNI, name, surname, building, number] := EMPLOYEES_ADM[DNI, name, surname, building,
officedesp, numberdesp];
• B := A * OFFICES;
• R := B[DNI, name, surname, area].
5.4. Extensions: outer joins
To finish the subject of relational algebra, we will analyze some extensions
useful combination.
The combinations that have been described obtain the tuples of the Cartesian-
not of two relations that satisfy a join condition.
It should be noted that tuples that have a null value for any of the attributes
butts contained in the join condition are always lost, because in
In these cases the join condition always evaluates to false.
In some cases, it may be interesting to make combinations of the data from two re-
relationships without loss of data from the starting relationships. Then,
outer joins are used. buy CPO targeted email list
R
DNI emp empname emp surname desp building desp number area
33,567,711 Marta Roca Marina 120 10
55,898,425 Carlos Buendia Diagonal 120 10
77,232,144 Elena Pla Marina 230 20
Outer joins between two relations T and S consist of va-
Combination variants that preserve all tuples in the result
of T, of S or of both relations. They can be of the following types:
1) The left outer join between two relations T and S, which
we denote as T[C]IS, keeps in the result all the tuples of the
T relationship.
2) The right outer join between two relations T and S, which
we denote as T[C] DS, keeps in the result all the tuples of
the s relationship.
3) Finally, the full outer join between two relations T
and S, which we denote as T[C]pS, preserves in the result all tu-
plas of T and all tuples of S.
The combinations have
explained in subsection 5.3.3
of this teaching unit.
 FUOC • 71Z799014MO 49 The relational model and relational algebra
These extensions also apply to the case of the natural combination between
two relations, T * S, namely:
a) The left outer natural join between two relations T and S, which
denoted as T *I S, preserves all tuples of relation T in the result.
b) The right outer natural join between two relations T and S, which is
denoted as T *D S, keeps in the result all the tuples of the relation S.
c) Finally, the full outer natural join between two relations T and S,
denoted as T *P S , preserves in the result all tuples of T and all
the tuples of S.
The tuples of a relation T that are preserved in the result R of a combination
external nation with another relation S, even though they do not satisfy the condition
combination, have null values in the R result for all attributes
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