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Article: On-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition

TitleOn-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition
Authors
KeywordsAutomatic speech recognition
Continuous density hidden markov models
Em algorithm
Recursive bayesian estimation
Speaker adaptation
Issue Date1998
PublisherIEEE.
Citation
IEEE Transactions on Speech and Audio Processing, 1998, v. 6 n. 4, p. 386-397 How to Cite?
AbstractWe extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models (HMMs) with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors' updating. As an example, by applying the method to an on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotically convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different model parameters. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments, and so on.
Persistent Identifierhttp://hdl.handle.net/10722/43643
ISSN
2007 Impact Factor: 2.291

 

DC FieldValueLanguage
dc.contributor.authorHuo, Qen_HK
dc.contributor.authorLee, CHen_HK
dc.date.accessioned2007-03-23T04:51:08Z-
dc.date.available2007-03-23T04:51:08Z-
dc.date.issued1998en_HK
dc.identifier.citationIEEE Transactions on Speech and Audio Processing, 1998, v. 6 n. 4, p. 386-397en_HK
dc.identifier.issn1063-6676en_HK
dc.identifier.urihttp://hdl.handle.net/10722/43643-
dc.description.abstractWe extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models (HMMs) with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors' updating. As an example, by applying the method to an on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotically convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different model parameters. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments, and so on.en_HK
dc.format.extent252321 bytes-
dc.format.extent27136 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherIEEE.en_HK
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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAutomatic speech recognitionen_HK
dc.subjectContinuous density hidden markov modelsen_HK
dc.subjectEm algorithmen_HK
dc.subjectRecursive bayesian estimationen_HK
dc.subjectSpeaker adaptationen_HK
dc.titleOn-line adaptive learning of the correlated continuous density hidden Markov models for speech recognitionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1063-6676&volume=6&issue=4&spage=386&epage=397&date=1998&atitle=On-line+adaptive+learning+of+the+correlated+continuous+density+hidden+Markov+models+for+speech+recognitionen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/89.701369en_HK
dc.identifier.scopuseid_2-s2.0-0032122203-
dc.identifier.hkuros33654-

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