How Is Individuality Expressed in Voice? An Introduction to Speech Production and Description for Speaker Classification

Dellwo, Volker; Huckvale, Mark; Ashby, Michael

doi:10.1007/978-3-540-74200-5_1

Cited by 33 publications

(33 citation statements)

References 3 publications

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“…a) Pitch Features: The default pitch as well as pitch range available to the speaker is influenced by the length and mass of the vocal folds in the larynx [24]. The main differences between speakers with respect to pitch is related to their gender and age.…”

Section: Feature Selectionmentioning

confidence: 99%

Prosodic and other Long-Term Features for Speaker Diarization

Friedland

Vinyals

Huang

et al. 2009

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Speaker diarization is defined as the task of determining "who spoke when" given an audio track and no other prior knowledge of any kind. The following article shows how a state-of-the-art speaker diarization system can be improved by combining traditional short-term features (MFCCs) with prosodic and other long-term features. First, we present a framework to study the speaker discriminability of 70 different long-term features. Then, we show how the top-ranked long-term features can be combined with short-term features to increase the accuracy of speaker diarization. The results were measured on standardized datasets (NIST RT) and show a consistent improvement of about 30% relative in diarization error rate compared to the best system presented at the NIST evaluation in 2007.

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Section: Feature Selectionmentioning

confidence: 99%

Prosodic and other Long-Term Features for Speaker Diarization

Friedland

Vinyals

Huang

et al. 2009

IEEE Trans. Audio Speech Lang. Process.

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“…Prosodic variations apply normally to more than one phoneme: syllables, words, phrases, clauses, etc. Since phonemes are known as speech segments in linguistic terms, these prosodic elements are also know as 'suprasegmental features', and they are usually analysed over sequences of segments or entire syllables [21,24].…”

Section: Prosody-based Parametersmentioning

confidence: 99%

Using Jitter and Shimmer in speaker verification

Farrús

Hernando

2009

IET Signal Process.

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Jitter and shimmer are measures of the fundamental frequency and amplitude cycle-to-cycle variations, respectively. Both features have been largely used for the description of pathological voices, and since they characterise some aspects concerning particular voices, they are expected to have a certain degree of speaker specificity. In the current work, jitter and shimmer are successfully used in a speaker verification experiment. Moreover, both measures are combined with spectral and prosodic features using several types of normalisation and fusion techniques in order to obtain better verification results. The overall speaker verification system is also improved by using histogram equalisation as a normalisation technique previous to fusing the features by SVM.

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“…Each of these processes encodes speaker-specific information. 1 This study investigated how between-speaker differences are reflected in temporal organizations of intensity contours in terms of intensity dynamics. Intensity dynamics were defined as the speed of increase in intensity from an amplitude envelope trough point to a consecutive peak point (henceforth, positive dynamics) and the speed of decrease in intensity from a peak to a consecutive trough point (henceforth, negative dynamics).…”

Section: Introductionmentioning

confidence: 99%

Between-speaker variability in temporal organizations of intensity contours

Dellwo

2017

The Journal of the Acoustical Society of America

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Intensity contours of speech signals were sub-divided into positive and negative dynamics. Positive dynamics were defined as the speed of increases in intensity from amplitude troughs to subsequent peaks, and negative dynamics as the speed of decreases in intensity from peaks to troughs. Mean, standard deviation, and sequential variability were measured for both dynamics in each sentence. Analyses showed that measures of both dynamics were separately classified and between-speaker variability was largely explained by measures of negative dynamics. This suggests that parts of the signal where intensity decreases from syllable peaks are more speaker-specific. Idiosyncratic articulation may explain such results. Abstract: Intensity contours of speech signals were sub-divided into positive and negative dynamics. Positive dynamics were defined as the speed of increases in intensity from amplitude troughs to subsequent peaks, and negative dynamics as the speed of decreases in intensity from peaks to troughs. Mean, standard deviation, and sequential variability were measured for both dynamics in each sentence. Analyses showed that measures of both dynamics were separately classified and betweenspeaker variability was largely explained by measures of negative dynamics. This suggests that parts of the signal where intensity decreases from syllable peaks are more speaker-specific. Idiosyncratic articulation may explain such results.

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How Is Individuality Expressed in Voice? An Introduction to Speech Production and Description for Speaker Classification

Cited by 33 publications

References 3 publications

Prosodic and other Long-Term Features for Speaker Diarization

Prosodic and other Long-Term Features for Speaker Diarization

Using Jitter and Shimmer in speaker verification

Between-speaker variability in temporal organizations of intensity contours

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