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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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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


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







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.


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.

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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.



Phonetic Lexical


k idiolect: l



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?


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

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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