Source and system features for speaker recognition using AANN models

Yegnanarayana, B.; Reddy, K. S.; Kishore, S.

doi:10.1109/icassp.2001.940854

Cited by 52 publications

(44 citation statements)

References 8 publications

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“…There are methods based on the coarse spectral structure associated with different phones in the speech signal [39]. Another techniques use the excitation function (the "fine" spectral details, such as [85]). Prosodic features representing aspects of the speech that occur over timescales larger than the individual phonemes can also be used, as well as the "Mannerisms" such as particular word choice or preferred phrases, or all kinds of other high-level behavioral characteristics.…”

Section: Speech Processingmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal Human Machine Interactions in Virtual and Augmented Reality

Chollet

Esposito

Gentès

et al. 2009

Multimodal Signals: Cognitive and Algorithmic Issues

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Abstract. Virtual worlds are developing rapidly over the Internet. They are visited by avatars and staffed with Embodied Conversational Agents (ECAs). An avatar is a representation of a physical person. Each person controls one or several avatars and usually receives feedback from the virtual world on an audio-visual display. Ideally, all senses should be used to feel fully embedded in a virtual world. Sound, vision and sometimes touch are the available modalities. This paper reviews the technological developments which enable audio-visual interactions in virtual and augmented reality worlds. Emphasis is placed on speech and gesture interfaces, including talking face analysis and synthesis.

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Section: Speech Processingmentioning

confidence: 99%

“…Speaker transformation techniques [28,39,85,40,2,7,77,62,16] might involve modifications of different aspects of the speech signal that carries the speaker's identity. We can cite different methods.…”

Section: Speech Processingmentioning

confidence: 99%

Multimodal Human Machine Interactions in Virtual and Augmented Reality

Chollet

Esposito

Gentès

et al. 2009

Multimodal Signals: Cognitive and Algorithmic Issues

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show abstract

“…There have been many efforts in the past to explore the source information that give a better SV performance when combined with the mel frequency cepstral coefficient (MFCC) feature of speech representing vocal tract aspect of speaker information. [5][6][7][8] This is due to the complementary nature of information captured by the source features in comparison to the system features. Also, as the excitation source features are less dependent on the amount of phonetic content, the duration of enrollment/testing can be less.…”

Section: Introductionmentioning

confidence: 99%

“…This reinforced the earlier findings of the literature. [5][6][7][8] Further, the improvement in performance is found to be more evident with a decrease in the duration of test data. The work on source features is extended to introduce mel power difference of spectrum in the subband (MPDSS) feature to capture the source information in a different manner.…”

Section: Introductionmentioning

confidence: 99%

Exploring different attributes of source information for speaker verification with limited test data

Das

Prasanna

2016

The Journal of the Acoustical Society of America

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This work explores mel power difference of spectrum in subband, residual mel frequency cepstral coefficient, and discrete cosine transform of the integrated linear prediction residual for speaker verification under limited test data conditions. These three source features are found to capture different attributes of source information, namely, periodicity, smoothed spectrum information, and shape of the glottal signal, respectively. On the NIST SRE 2003 database, the proposed combination of the three source features performs better [equal error rate (EER): 20.19%, decision cost function (DCF): 0.3759] than the mel frequency cepstral coefficient feature (EER: 22.31%, DCF: 0.4128) for 2 s duration of test segments.

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“…However, experiments exist [9] where it is shown that human beings are able to recognize the identity of the speaker listening to residual signals of LP analysis. Based on this fact several authors have evaluated the usefulness of the LPC-residue and have found that although the identification rates using this kind of information alone does not perform as well as the LPderived cepstral coefficients, a combination of both can improve the results [20,12,14,22,11]. 2.…”

Section: Introductionmentioning

confidence: 99%

On the Usefulness of Linear and Nonlinear Prediction Residual Signals for Speaker Recognition

Faundez-Zanuy¹

Advances in Nonlinear Speech Processing

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This paper compares the identification rates of a speaker recognition system using several parameterizations, with special emphasis on the residual signal obtained from linear and nonlinear predictive analysis. It is found that the residual signal is still useful even when using a high dimensional linear predictive analysis. On the other hand, it is shown that the residual signal of a nonlinear analysis contains less useful information, even for a prediction order of 10, than the linear residual signal. This shows the inability of the linear models to cope with nonlinear dependences present in speech signals, which are useful for recognition purposes.

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Source and system features for speaker recognition using AANN models

Cited by 52 publications

References 8 publications

Multimodal Human Machine Interactions in Virtual and Augmented Reality

Multimodal Human Machine Interactions in Virtual and Augmented Reality

Exploring different attributes of source information for speaker verification with limited test data

On the Usefulness of Linear and Nonlinear Prediction Residual Signals for Speaker Recognition

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