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Conference Paper: Sequential Bayesian learning of CDHMM based on finite mixture approximation of its prior/posterior density

TitleSequential Bayesian learning of CDHMM based on finite mixture approximation of its prior/posterior density
Authors
Issue Date1997
PublisherIEEE.
Citation
IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings, Santa Barbara, California, 14-17 December 1997, p. 373-380 How to Cite?
AbstractProposes a sequential Bayesian learning strategy of a continuous-density hidden Markov model (CDHMM) based on a finite mixture approximation of its prior/posterior density. The initial prior density of the CDHMM is assumed to be a finite mixture of natural conjugate prior probability density functions (PDFs) of the complete-data density. With the new observation data, the true posterior PDF is approximated by the same type of finite-mixture PDFs which retain the required most significant terms in the true posterior density according to their contribution to the corresponding Bayesian predictive density by using an N-best beam search algorithm. Then, the updated mixture PDF is used in the VBPC (Viterbi Bayesian predictive classification) method to deal with unknown mismatches in robust speech recognition. Experimental results on a speaker-independent recognition task of isolated Japanese digits confirm the viability and the usefulness of the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/45603

 

DC FieldValueLanguage
dc.contributor.authorJiang, Hen_HK
dc.contributor.authorHirose, Ken_HK
dc.contributor.authorHuo, Qen_HK
dc.date.accessioned2007-10-30T06:30:05Z-
dc.date.available2007-10-30T06:30:05Z-
dc.date.issued1997en_HK
dc.identifier.citationIEEE Workshop on Automatic Speech Recognition and Understanding Proceedings, Santa Barbara, California, 14-17 December 1997, p. 373-380en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45603-
dc.description.abstractProposes a sequential Bayesian learning strategy of a continuous-density hidden Markov model (CDHMM) based on a finite mixture approximation of its prior/posterior density. The initial prior density of the CDHMM is assumed to be a finite mixture of natural conjugate prior probability density functions (PDFs) of the complete-data density. With the new observation data, the true posterior PDF is approximated by the same type of finite-mixture PDFs which retain the required most significant terms in the true posterior density according to their contribution to the corresponding Bayesian predictive density by using an N-best beam search algorithm. Then, the updated mixture PDF is used in the VBPC (Viterbi Bayesian predictive classification) method to deal with unknown mismatches in robust speech recognition. Experimental results on a speaker-independent recognition task of isolated Japanese digits confirm the viability and the usefulness of the proposed method.en_HK
dc.format.extent420352 bytes-
dc.format.extent7254 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rights©1997 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleSequential Bayesian learning of CDHMM based on finite mixture approximation of its prior/posterior densityen_HK
dc.typeConference_Paperen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ASRU.1997.659113en_HK
dc.identifier.hkuros38218-

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