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Statistical model-based voice activity detection using support vector machine

DC Field Value Language
dc.contributor.authorJo, Q.-H.-
dc.contributor.authorChang, J.-H.-
dc.contributor.authorShin, J.W.-
dc.contributor.authorKim, N.S.-
dc.date.accessioned2024-05-13T00:00:32Z-
dc.date.available2024-05-13T00:00:32Z-
dc.date.created2023-11-21-
dc.date.created2023-11-21-
dc.date.issued2009-05-
dc.identifier.citationIET Signal Processing, Vol.3 No.3, pp.205-210-
dc.identifier.issn1751-9675-
dc.identifier.urihttps://hdl.handle.net/10371/201480-
dc.description.abstractFrom an investigation of a statistical model-based voice activity detection (VAD), it is discovered that a simple heuristic way like a geometric mean has been adopted for a decision rule based on the likelihood ratio (LR) test. For a successful VAD operation, the authors first review the behaviour mechanism of support vector machine (SVM) and then propose a novel technique, which employs the decision function of SVM using the LRs, while the conventional techniques perform VAD comparing the geometric mean of the LRs with a given threshold value. The proposed SVM-based VAD is compared to the conventional statistical model-based scheme, and shows better performances in various noise environments. © 2009 The Institution of Engineering and Technology.-
dc.language영어-
dc.publisherInstitution of Engineering and Technology-
dc.titleStatistical model-based voice activity detection using support vector machine-
dc.typeArticle-
dc.identifier.doi10.1049/iet-spr.2008.0128-
dc.citation.journaltitleIET Signal Processing-
dc.identifier.wosid000266438600004-
dc.identifier.scopusid2-s2.0-65549106422-
dc.citation.endpage210-
dc.citation.number3-
dc.citation.startpage205-
dc.citation.volume3-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorKim, N.S.-
dc.type.docTypeArticle-
dc.description.journalClass1-
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