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Conference Paper: Improving Viterbi Bayesian predictive classification via sequentialBayesian learning in robust speech recognition

TitleImproving Viterbi Bayesian predictive classification via sequentialBayesian learning in robust speech recognition
Authors
KeywordsEngineering
Electrical engineering
Issue Date1998
PublisherIEEE.
Citation
IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Seattle, WA, USA, 12-15 May 1998, v. 1, p. 77-80 How to Cite?
AbstractWe extend our previously proposed Viterbi Bayesian predictive classification (VBPC) algorithm to accommodate a new class of prior probability density function (PDF) for continuous density hidden Markov model (CDHMM) based robust speech recognition. The initial prior PDF of CDHMM is assumed to be a finite mixture of natural conjugate prior PDF's of its complete-data density. With the new observation data, the true posterior PDF is approximated by the same type of finite mixture PDF's which retain the required most significant terms in the true posterior density according to their contribution to the corresponding predictive density. Then the updated mixture PDF is used to improve the VBPC performance. The experimental results on a speaker-independent recognition task of isolated Japanese digits confirm the viability and the usefulness of the proposed technique.
Persistent Identifierhttp://hdl.handle.net/10722/45595
ISSN

 

DC FieldValueLanguage
dc.contributor.authorJiang, Hen_HK
dc.contributor.authorHirose, Ken_HK
dc.contributor.authorHuo, Qen_HK
dc.date.accessioned2007-10-30T06:29:56Z-
dc.date.available2007-10-30T06:29:56Z-
dc.date.issued1998en_HK
dc.identifier.citationIEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Seattle, WA, USA, 12-15 May 1998, v. 1, p. 77-80en_HK
dc.identifier.issn1520-6149en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45595-
dc.description.abstractWe extend our previously proposed Viterbi Bayesian predictive classification (VBPC) algorithm to accommodate a new class of prior probability density function (PDF) for continuous density hidden Markov model (CDHMM) based robust speech recognition. The initial prior PDF of CDHMM is assumed to be a finite mixture of natural conjugate prior PDF's of its complete-data density. With the new observation data, the true posterior PDF is approximated by the same type of finite mixture PDF's which retain the required most significant terms in the true posterior density according to their contribution to the corresponding predictive density. Then the updated mixture PDF is used to improve the VBPC performance. The experimental results on a speaker-independent recognition task of isolated Japanese digits confirm the viability and the usefulness of the proposed technique.en_HK
dc.format.extent429036 bytes-
dc.format.extent7254 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectEngineeringen_HK
dc.subjectElectrical engineeringen_HK
dc.titleImproving Viterbi Bayesian predictive classification via sequentialBayesian learning in robust speech recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1520-6149&volume=1&spage=77&epage=80&date=1998&atitle=Improving+Viterbi+Bayesian+predictive+classification+via+sequentialBayesian+learning+in+robust+speech+recognitionen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICASSP.1998.674371en_HK
dc.identifier.hkuros33652-

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