Voice source cepstrum coefficients for speaker identification

Guðnason, Jón; Brookes, Mike

doi:10.1109/icassp.2008.4518736

Cited by 52 publications

(45 citation statements)

References 21 publications

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“…This is in concurrence with studies that compare vocal tract and voice source features both for other tasks [18,19] and cognitive workload [22]. Never the less the performance of the vocal tract parameters can be improved by fusing the two parameters at the output level.…”

Section: Discussionsupporting

confidence: 64%

“…Voice source features can be extracted indirectly from the speech signal via covariance analysis and cepstrum processing without relying on inverse filtering [18,19]. Various methods for inverse filtering have however been implemented and evaluated [20] and feature extraction methods based on the glottal flow estimate have been proposed [21,22,23].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

Magnúsdóttir

Borský

Meier

et al. 2017

Period. Polytech. Elec. Eng. Comp. Sci.

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IntroductionMonitoring cognitive workload in individuals that are performing safety-critical jobs has huge potential, for example in aviation [1]. The relationship between cognitive workload and performance has been well studied [2] and the connection between cognitive workload and physical health has also been highlighted [3]. Clearly, it is crucial to manage cognitive workload in the modern day work environment, both in terms of performance and health. Speech processing offers the ability to monitor cognitive workload in a non-intrusive way, compared to measures such as blood pressure, heart rate, electroencephalogram or electrocardiogram. Measuring voice is easy and recent work has shown very promising results in classifying cognitive workload levels using the speech signal [4][5][6]. Successful design and implementation of such a method would provide a powerful tool to developers of cognitive infocommunications systems [7].The main objective of this work is to provide an independent verification of whether there is a link between increased cognitive workload and changes in the speech signal and, if this link exists, to characterize what part of voice is mostly affected. To do this we conducted a cognitive workload experiment where the set tasks were primarily solved using speech from 98 participants. Each participant read a standard passage of text on a computer screen and solved Stroop tasks with three difficulty levels [8]. A separate classifier of the three difficulty levels was trained for each speaker based on two sets of voice parameters comprising of the vocal tract (VT) and voice source (VS) features respectively. The conclusion is that task load does affect the speech signal and that vocal tract parameters are a better indicator of task level than voice source parameters. The context of the work is presented in Section 2 and the speech processing approach is described in Section 3. The experimental methodology and detailed results are given in Section 4 and 5. The results are summarized in Section 6 and the discussion is continued in Section 7.

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Section: Discussionsupporting

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

Magnúsdóttir

Borský

Meier

et al. 2017

Period. Polytech. Elec. Eng. Comp. Sci.

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“…Also, baseline (modal) values of voice quality measurements such as NAQ vary significantly across speakers [28]. Spontaneous, continuous speech, typical of conversations, has voice quality characteristics that differ significantly enough across speakers that they may be used as features for automatic speaker identification systems [50,51]. For these reasons, in order to measure voice quality of a given speech segment, the measure must be normalized for the underlying speaker variation regarding age, mood, conversational context, fatigue, and other factors.…”

Section: Acoustic Measures Of Stress and Voice Qualitymentioning

confidence: 99%

Physical task stress and speaker variability in voice quality

Godin

Hansen

2015

J AUDIO SPEECH MUSIC PROC.

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The presence of physical task stress induces changes in the speech production system which in turn produces changes in speaking behavior. This results in measurable acoustic correlates including changes to formant center frequencies, breath pause placement, and fundamental frequency. Many of these changes are due to the subject's internal competition between speaking and breathing during the performance of the physical task, which has a corresponding impact on muscle control and airflow within the glottal excitation structure as well as vocal tract articulatory structure. This study considers the effect of physical task stress on voice quality. Three signal processingbased values which include (i) the normalized amplitude quotient (NAQ), (ii) the harmonic richness factor (HRF), and (iii) the fundamental frequency are used to measure voice quality. The effects of physical stress on voice quality depend on the speaker as well as the specific task. While some speakers do not exhibit changes in voice quality, a subset exhibits changes in NAQ and HRF measures of similar magnitude to those observed in studies of soft, loud, and pressed speech. For those speakers demonstrating voice quality changes, the observed changes tend toward breathy or soft voicing as observed in other studies. The effect of physical stress on the fundamental frequency is correlated with the effect of physical stress on the HRF (r = −0.34) and the NAQ (r = −0.53). Also, the inter-speaker variation in baseline NAQ is significantly higher than the variation in NAQ induced by physical task stress. The results illustrate systematic changes in speech production under physical task stress, which in theory will impact subsequent speech technology such as speech recognition, speaker recognition, and voice diarization systems.

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“…2 The VS estimate is also used for analyzing pathological voices 3 and features extracted from its shape for speaker identification (SID). [4][5][6] Further, studies like Ref. 7 show that the VS pulse shape influences the perceived voice quality.…”

Section: Introductionmentioning

confidence: 99%

Voice source characterization using pitch synchronous discrete cosine transform for speaker identification

Ramakrishnan

Abhiram

Prasanna

2015

The Journal of the Acoustical Society of America

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A characterization of the voice source (VS) signal by the pitch synchronous (PS) discrete cosine transform (DCT) is proposed. With the integrated linear prediction residual (ILPR) as the VS estimate, the PS DCT of the ILPR is evaluated as a feature vector for speaker identification (SID). On TIMIT and YOHO databases, using a Gaussian mixture model (GMM)-based classifier, it performs on par with existing VS-based features. On the NIST 2003 database, fusion with a GMM-based classifier using MFCC features improves the identification accuracy by 12% in absolute terms, proving that the proposed characterization has good promise as a feature for SID studies.

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Voice source cepstrum coefficients for speaker identification

Cited by 52 publications

References 21 publications

Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

Physical task stress and speaker variability in voice quality

Voice source characterization using pitch synchronous discrete cosine transform for speaker identification

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