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Voice activity detection based on statistical models and machine learning approaches

DC Field Value Language
dc.contributor.authorShin, Jong Won-
dc.contributor.authorChang, Joon-Hyuk-
dc.contributor.authorKim, Nam Soo-
dc.date.accessioned2024-05-13T00:00:13Z-
dc.date.available2024-05-13T00:00:13Z-
dc.date.created2021-12-17-
dc.date.created2021-12-17-
dc.date.issued2010-07-
dc.identifier.citationComputer Speech and Language, Vol.24 No.3, pp.515-530-
dc.identifier.issn0885-2308-
dc.identifier.urihttps://hdl.handle.net/10371/201474-
dc.description.abstractThe voice activity detectors (VADs) based on statistical models have shown impressive performances especially when fairly precise statistical models arc employed. Moreover, the accuracy of the VAD utilizing statistical models can be significantly improved when machine-learning techniques are adopted to provide prior knowledge for speech characteristics. In the first part of this paper, we introduce a more accurate and flexible statistical model, the generalized gamma distribution (G Gamma D) as a new model in the VAD based on the likelihood ratio test. In practice, parameter estimation algorithm based on maximum likelihood principle is also presented. Experimental results show that the VAD algorithm implemented based on G Gamma D outperform those adopting the conventional Laplacian and Gamma distributions. In the second part of this paper, we introduce machine learning techniques such as a minimum classification error (MCE) and support vector machine (SVM) to exploit automatically prior knowledge obtained from the speech database, which can enhance the performance of the VAD. Firstly, we present a discriminative weight training method based on the MCE criterion. In this approach, the VAD decision rule becomes the geometric mean of optimally weighted likelihood ratios. Secondly, the SVM-based approach is introduced to assist the VAD based on statistical models. In this algorithm, the SVM efficiently classifies the input signal into two classes which arc voice active and voice inactive regions with nonlinear boundary. Experimental results show that these training-based approaches can effectively enhance the performance of the VAD. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.-
dc.language영어-
dc.publisherAcademic Press-
dc.titleVoice activity detection based on statistical models and machine learning approaches-
dc.typeArticle-
dc.identifier.doi10.1016/j.csl.2009.02.003-
dc.citation.journaltitleComputer Speech and Language-
dc.identifier.wosid000277330400007-
dc.identifier.scopusid2-s2.0-77950091897-
dc.citation.endpage530-
dc.citation.number3-
dc.citation.startpage515-
dc.citation.volume24-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorKim, Nam Soo-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordPlusSPEECH ENHANCEMENT-
dc.subject.keywordAuthorVoice activity detection-
dc.subject.keywordAuthorStatistical modeling-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorPrior knowledge-
dc.subject.keywordAuthorLikelihood ratio test-
dc.subject.keywordAuthorGeneralized gamma-
dc.subject.keywordAuthorMinimum classification error-
dc.subject.keywordAuthorSupport vector machine-
dc.subject.keywordAuthorA posteriori SNR-
dc.subject.keywordAuthorA priori SNR-
dc.subject.keywordAuthorPredicted SNR-
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