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Article: On-line adaptive learning of the continuous density hidden Markov model based on approximate recursive Bayes estimate

TitleOn-line adaptive learning of the continuous density hidden Markov model based on approximate recursive Bayes estimate
Authors
KeywordsRecursive bayesian estimation
Incremental maximum likelihood estimation
Hidden markov model
Em algorithm
Automatic speech recognition
Issue Date1997
PublisherIEEE.
Citation
IEEE Transactions on Speech and Audio Processing, 1997, v. 5 n. 2, p. 161-172 How to Cite?
AbstractWe present a framework of quasi-Bayes (QB) learning of the parameters of the continuous density hidden Markov model (CDHMM) with Gaussian mixture state observation densities. The QB formulation is based on the theory of recursive Bayesian inference. The QB algorithm is designed to incrementally update the hyperparameters of the approximate posterior distribution and the CDHMM parameters simultaneously. By further introducing a simple forgetting mechanism to adjust the contribution of previously observed sample utterances, the algorithm is adaptive in nature and capable of performing an online adaptive learning using only the current sample utterance. It can, thus, be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, and transducers. As an example, the QB learning framework is applied to on-line speaker adaptation and its viability is confirmed in a series of comparative experiments using a 26-letter English alphabet vocabulary.
Persistent Identifierhttp://hdl.handle.net/10722/43642
ISSN
2007 Impact Factor: 2.291

 

DC FieldValueLanguage
dc.contributor.authorHuo, Qen_HK
dc.contributor.authorLee, CHen_HK
dc.date.accessioned2007-03-23T04:51:07Z-
dc.date.available2007-03-23T04:51:07Z-
dc.date.issued1997en_HK
dc.identifier.citationIEEE Transactions on Speech and Audio Processing, 1997, v. 5 n. 2, p. 161-172en_HK
dc.identifier.issn1063-6676en_HK
dc.identifier.urihttp://hdl.handle.net/10722/43642-
dc.description.abstractWe present a framework of quasi-Bayes (QB) learning of the parameters of the continuous density hidden Markov model (CDHMM) with Gaussian mixture state observation densities. The QB formulation is based on the theory of recursive Bayesian inference. The QB algorithm is designed to incrementally update the hyperparameters of the approximate posterior distribution and the CDHMM parameters simultaneously. By further introducing a simple forgetting mechanism to adjust the contribution of previously observed sample utterances, the algorithm is adaptive in nature and capable of performing an online adaptive learning using only the current sample utterance. It can, thus, be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, and transducers. As an example, the QB learning framework is applied to on-line speaker adaptation and its viability is confirmed in a series of comparative experiments using a 26-letter English alphabet vocabulary.en_HK
dc.format.extent236166 bytes-
dc.format.extent27136 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
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.subjectRecursive bayesian estimationen_HK
dc.subjectIncremental maximum likelihood estimationen_HK
dc.subjectHidden markov modelen_HK
dc.subjectEm algorithmen_HK
dc.subjectAutomatic speech recognitionen_HK
dc.titleOn-line adaptive learning of the continuous density hidden Markov model based on approximate recursive Bayes estimateen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1063-6676&volume=5&issue=2&spage=161&epage=172&date=1997&atitle=On-line+adaptive+learning+of+the+continuous+density+hidden+Markov+model+based+on+approximate+recursive+Bayes+estimateen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/89.554778en_HK
dc.identifier.scopuseid_2-s2.0-0031103160-
dc.identifier.hkuros31110-

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