IEICE TRANSACTIONS on Information
Voice Activity Detection Based on High Order Statistics and Online EM Algorithm
Summary :
A new online, unsupervised voice activity detection (VAD) method is proposed. The method is based on a feature derived from high-order statistics (HOS), enhanced by a second metric based on normalized autocorrelation peaks to improve its robustness to non-Gaussian noises. This feature is also oriented for discriminating between close-talk and far-field speech, thus providing a VAD method in the context of human-to-human interaction independent of the energy level. The classification is done by an online variation of the Expectation-Maximization (EM) algorithm, to track and adapt to noise variations in the speech signal. Performance of the proposed method is evaluated on an in-house data and on CENSREC-1-C, a publicly available database used for VAD in the context of automatic speech recognition (ASR). On both test sets, the proposed method outperforms a simple energy-based algorithm and is shown to be more robust against the change in speech sparsity, SNR variability and the noise type.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.12 pp.2854-2861
- Publication Date
- 2008/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.12.2854
- Type of Manuscript
- PAPER
- Category
- Speech and Hearing
Authors
Keyword
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David COURNAPEAU, Tatsuya KAWAHARA, "Voice Activity Detection Based on High Order Statistics and Online EM Algorithm" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 12, pp. 2854-2861, December 2008, doi: 10.1093/ietisy/e91-d.12.2854.
Abstract: A new online, unsupervised voice activity detection (VAD) method is proposed. The method is based on a feature derived from high-order statistics (HOS), enhanced by a second metric based on normalized autocorrelation peaks to improve its robustness to non-Gaussian noises. This feature is also oriented for discriminating between close-talk and far-field speech, thus providing a VAD method in the context of human-to-human interaction independent of the energy level. The classification is done by an online variation of the Expectation-Maximization (EM) algorithm, to track and adapt to noise variations in the speech signal. Performance of the proposed method is evaluated on an in-house data and on CENSREC-1-C, a publicly available database used for VAD in the context of automatic speech recognition (ASR). On both test sets, the proposed method outperforms a simple energy-based algorithm and is shown to be more robust against the change in speech sparsity, SNR variability and the noise type.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.12.2854/_p
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@ARTICLE{e91-d_12_2854,
author={David COURNAPEAU, Tatsuya KAWAHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Voice Activity Detection Based on High Order Statistics and Online EM Algorithm},
year={2008},
volume={E91-D},
number={12},
pages={2854-2861},
abstract={A new online, unsupervised voice activity detection (VAD) method is proposed. The method is based on a feature derived from high-order statistics (HOS), enhanced by a second metric based on normalized autocorrelation peaks to improve its robustness to non-Gaussian noises. This feature is also oriented for discriminating between close-talk and far-field speech, thus providing a VAD method in the context of human-to-human interaction independent of the energy level. The classification is done by an online variation of the Expectation-Maximization (EM) algorithm, to track and adapt to noise variations in the speech signal. Performance of the proposed method is evaluated on an in-house data and on CENSREC-1-C, a publicly available database used for VAD in the context of automatic speech recognition (ASR). On both test sets, the proposed method outperforms a simple energy-based algorithm and is shown to be more robust against the change in speech sparsity, SNR variability and the noise type.},
keywords={},
doi={10.1093/ietisy/e91-d.12.2854},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Voice Activity Detection Based on High Order Statistics and Online EM Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 2854
EP - 2861
AU - David COURNAPEAU
AU - Tatsuya KAWAHARA
PY - 2008
DO - 10.1093/ietisy/e91-d.12.2854
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2008
AB - A new online, unsupervised voice activity detection (VAD) method is proposed. The method is based on a feature derived from high-order statistics (HOS), enhanced by a second metric based on normalized autocorrelation peaks to improve its robustness to non-Gaussian noises. This feature is also oriented for discriminating between close-talk and far-field speech, thus providing a VAD method in the context of human-to-human interaction independent of the energy level. The classification is done by an online variation of the Expectation-Maximization (EM) algorithm, to track and adapt to noise variations in the speech signal. Performance of the proposed method is evaluated on an in-house data and on CENSREC-1-C, a publicly available database used for VAD in the context of automatic speech recognition (ASR). On both test sets, the proposed method outperforms a simple energy-based algorithm and is shown to be more robust against the change in speech sparsity, SNR variability and the noise type.
ER -
English
