Convex Combination of Multiple Statistical Models with Application to VAD

Theodoros Petsatodis, Christos Boukis, Fotios Talantzis, Zheng-Hua Tan, Ramjee Prasad

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

21 Citationer (Scopus)

Resumé

This paper proposes a robust Voice Activity Detector (VAD) based on the observation that the distribution of speech captured with far-field microphones is highly varying, depending on the noise and reverberation conditions. The proposed VAD employs a convex combination scheme comprising three statistical distributions - a Gaussian, a Laplacian, and a two-sided Gamma - to effectively model captured speech. This scheme shows increased ability to adapt to dynamic acoustic environments. The contribution of each distribution to this convex combination is automatically adjusted based on the statistical characteristics of the instantaneous audio input. To further improve the performance of the system, an adaptive threshold is introduced, while a decision-smoothing scheme caters to the intra-frame correlation of speech signals. Extensive experiments under realistic scenarios support the proposed approach of combining several models for increased adaptation and performance.
OriginalsprogEngelsk
TidsskriftI E E E Transactions on Audio, Speech and Language Processing
Vol/bind19
Udgave nummer8
Sider (fra-til)2314-2327
ISSN1558-7916
DOI
StatusUdgivet - nov. 2011

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Detectors
detectors
reverberation
statistical distributions
microphones
smoothing
far fields
Reverberation
Adaptive systems
Microphones
thresholds
Acoustic noise
acoustics
Acoustics
Statistical Models
Experiments

Citer dette

Petsatodis, Theodoros ; Boukis, Christos ; Talantzis, Fotios ; Tan, Zheng-Hua ; Prasad, Ramjee. / Convex Combination of Multiple Statistical Models with Application to VAD. I: I E E E Transactions on Audio, Speech and Language Processing. 2011 ; Bind 19, Nr. 8. s. 2314-2327.
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title = "Convex Combination of Multiple Statistical Models with Application to VAD",
abstract = "This paper proposes a robust Voice Activity Detector (VAD) based on the observation that the distribution of speech captured with far-field microphones is highly varying, depending on the noise and reverberation conditions. The proposed VAD employs a convex combination scheme comprising three statistical distributions - a Gaussian, a Laplacian, and a two-sided Gamma - to effectively model captured speech. This scheme shows increased ability to adapt to dynamic acoustic environments. The contribution of each distribution to this convex combination is automatically adjusted based on the statistical characteristics of the instantaneous audio input. To further improve the performance of the system, an adaptive threshold is introduced, while a decision-smoothing scheme caters to the intra-frame correlation of speech signals. Extensive experiments under realistic scenarios support the proposed approach of combining several models for increased adaptation and performance.",
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Convex Combination of Multiple Statistical Models with Application to VAD. / Petsatodis, Theodoros; Boukis, Christos ; Talantzis, Fotios ; Tan, Zheng-Hua; Prasad, Ramjee.

I: I E E E Transactions on Audio, Speech and Language Processing, Bind 19, Nr. 8, 11.2011, s. 2314-2327.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Convex Combination of Multiple Statistical Models with Application to VAD

AU - Petsatodis, Theodoros

AU - Boukis, Christos

AU - Talantzis, Fotios

AU - Tan, Zheng-Hua

AU - Prasad, Ramjee

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N2 - This paper proposes a robust Voice Activity Detector (VAD) based on the observation that the distribution of speech captured with far-field microphones is highly varying, depending on the noise and reverberation conditions. The proposed VAD employs a convex combination scheme comprising three statistical distributions - a Gaussian, a Laplacian, and a two-sided Gamma - to effectively model captured speech. This scheme shows increased ability to adapt to dynamic acoustic environments. The contribution of each distribution to this convex combination is automatically adjusted based on the statistical characteristics of the instantaneous audio input. To further improve the performance of the system, an adaptive threshold is introduced, while a decision-smoothing scheme caters to the intra-frame correlation of speech signals. Extensive experiments under realistic scenarios support the proposed approach of combining several models for increased adaptation and performance.

AB - This paper proposes a robust Voice Activity Detector (VAD) based on the observation that the distribution of speech captured with far-field microphones is highly varying, depending on the noise and reverberation conditions. The proposed VAD employs a convex combination scheme comprising three statistical distributions - a Gaussian, a Laplacian, and a two-sided Gamma - to effectively model captured speech. This scheme shows increased ability to adapt to dynamic acoustic environments. The contribution of each distribution to this convex combination is automatically adjusted based on the statistical characteristics of the instantaneous audio input. To further improve the performance of the system, an adaptive threshold is introduced, while a decision-smoothing scheme caters to the intra-frame correlation of speech signals. Extensive experiments under realistic scenarios support the proposed approach of combining several models for increased adaptation and performance.

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KW - convex combination

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