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Conference Paper: On-line Bayes adaptation of SCHMM parameters for speech recognition

TitleOn-line Bayes adaptation of SCHMM parameters for speech recognition
Authors
KeywordsEngineering
Electrical engineering
Issue Date1995
PublisherIEEE.
Citation
IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Detroit, MI, USA, 9-12 May 1995, v. 1, p. 708-711 How to Cite?
AbstractOn-line adaptation of semi-continuous (or tied mixture) hidden Markov model (SCHMM) is studied. A theoretical formulation of the segmental quasi-Bayes learning of the mixture coefficients in SCHMM for speech recognition is presented. The practical issues related to the use of this algorithm for on-line speaker adaptation are addressed. A pragmatic on-line adaptation approach to combine the long-term adaptation of the mixture coefficients and the short-term adaptation of the mean vectors of the Gaussian mixture components are also proposed. The viability of these techniques are confirmed in a series of comparative experiments using a 26-word English alphabet vocabulary.
Persistent Identifierhttp://hdl.handle.net/10722/45620
ISSN

 

DC FieldValueLanguage
dc.contributor.authorHuo, Qen_HK
dc.contributor.authorChan, Cen_HK
dc.date.accessioned2007-10-30T06:30:27Z-
dc.date.available2007-10-30T06:30:27Z-
dc.date.issued1995en_HK
dc.identifier.citationIEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Detroit, MI, USA, 9-12 May 1995, v. 1, p. 708-711en_HK
dc.identifier.issn1520-6149en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45620-
dc.description.abstractOn-line adaptation of semi-continuous (or tied mixture) hidden Markov model (SCHMM) is studied. A theoretical formulation of the segmental quasi-Bayes learning of the mixture coefficients in SCHMM for speech recognition is presented. The practical issues related to the use of this algorithm for on-line speaker adaptation are addressed. A pragmatic on-line adaptation approach to combine the long-term adaptation of the mixture coefficients and the short-term adaptation of the mean vectors of the Gaussian mixture components are also proposed. The viability of these techniques are confirmed in a series of comparative experiments using a 26-word English alphabet vocabulary.en_HK
dc.format.extent392347 bytes-
dc.format.extent3669 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©1995 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.titleOn-line Bayes adaptation of SCHMM parameters for speech recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1520-6149&volume=1&spage=708&epage=711&date=1995&atitle=On-line+Bayes+adaptation+of+SCHMM+parameters+for+speech+recognitionen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICASSP.1995.479792en_HK
dc.identifier.hkuros531-

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