File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: On-line adaptation of the SCHMM parameters based on the segmental quasi-bayes learning for speech recognition

TitleOn-line adaptation of the SCHMM parameters based on the segmental quasi-bayes learning for speech recognition
Authors
Huo, QChan, CLee, CH
Issue Date1996
PublisherIEEE.
Citation
IEEE Transactions on Speech and Audio Processing, 1996, v. 4 n. 2, p. 141-144 How to Cite?
DOI: http://dx.doi.org/10.1109/89.486065
AbstractOn-line quasi-Bayes adaptation of the mixture coefficients and mean vectors in semicontinuous hidden Markov model (SCHMM) is studied. The viability of the proposed algorithm is confirmed and the related practical issues are addressed in a specific application of on-line speaker adaptation using a 26-word English alphabet vocabulary.
Persistent Identifierhttp://hdl.handle.net/10722/43628
ISSN
1063-6676
ISI Accession Number ID
WOS:A1996UC34700009

 

DC FieldValueLanguage
dc.contributor.authorHuo, Qen_HK
dc.contributor.authorChan, Cen_HK
dc.contributor.authorLee, CHen_HK
dc.date.accessioned2007-03-23T04:50:48Z-
dc.date.available2007-03-23T04:50:48Z-
dc.date.issued1996en_HK
dc.identifier.citationIEEE Transactions on Speech and Audio Processing, 1996, v. 4 n. 2, p. 141-144en_HK
dc.identifier.issn1063-6676en_HK
dc.identifier.urihttp://hdl.handle.net/10722/43628-
dc.description.abstractOn-line quasi-Bayes adaptation of the mixture coefficients and mean vectors in semicontinuous hidden Markov model (SCHMM) is studied. The viability of the proposed algorithm is confirmed and the related practical issues are addressed in a specific application of on-line speaker adaptation using a 26-word English alphabet vocabulary.en_HK
dc.format.extent572651 bytes-
dc.format.extent2668 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Speech and Audio Processing-
dc.rights©1996 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.-
dc.titleOn-line adaptation of the SCHMM parameters based on the segmental quasi-bayes learning for speech recognitionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1063-6676&volume=4&issue=2&spage=141&epage=144&date=1996&atitle=On-line+adaptation+of+the+SCHMM+parameters+based+on+the+segmental+quasi-bayes+learning+for+speech+recognitionen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/89.486065-
dc.identifier.scopuseid_2-s2.0-0030105005-
dc.identifier.hkuros10591-
dc.identifier.isiWOS:A1996UC34700009-
dc.identifier.issnl1063-6676-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats