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Conference Paper: A study of prior sensitivity for Bayesian predictive classificationbased robust speech recognition

TitleA study of prior sensitivity for Bayesian predictive classificationbased robust speech recognition
Authors
KeywordsEngineering
Electrical engineering
Issue Date1998
PublisherIEEE.
Citation
IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Seattle, WA, USA, 12-15 May 1998, v. 2, p. 741-744 How to Cite?
AbstractWe previously introduced a new Bayesian predictive classification (BPC) approach to robust speech recognition and showed that the BPC is capable of coping with many types of distortions. We also learned that the efficacy of the BPC algorithm is influenced by the appropriateness of the prior distribution for the mismatch being compensated. If the prior distribution fails to characterize the variability reflected in the model parameters, then the BPC will not help much. We show how the knowledge and/or experience of the interaction between the speech signal and the possible mismatch guide us to obtain a better prior distribution which improves the performance of the BPC approach.
Persistent Identifierhttp://hdl.handle.net/10722/45596
ISSN

 

DC FieldValueLanguage
dc.contributor.authorHuo, Qen_HK
dc.contributor.authorLee, CHen_HK
dc.date.accessioned2007-10-30T06:29:57Z-
dc.date.available2007-10-30T06:29:57Z-
dc.date.issued1998en_HK
dc.identifier.citationIEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Seattle, WA, USA, 12-15 May 1998, v. 2, p. 741-744en_HK
dc.identifier.issn1520-6149en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45596-
dc.description.abstractWe previously introduced a new Bayesian predictive classification (BPC) approach to robust speech recognition and showed that the BPC is capable of coping with many types of distortions. We also learned that the efficacy of the BPC algorithm is influenced by the appropriateness of the prior distribution for the mismatch being compensated. If the prior distribution fails to characterize the variability reflected in the model parameters, then the BPC will not help much. We show how the knowledge and/or experience of the interaction between the speech signal and the possible mismatch guide us to obtain a better prior distribution which improves the performance of the BPC approach.en_HK
dc.format.extent499779 bytes-
dc.format.extent7254 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©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.subjectEngineeringen_HK
dc.subjectElectrical engineeringen_HK
dc.titleA study of prior sensitivity for Bayesian predictive classificationbased robust speech recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1520-6149&volume=2&spage=741&epage=744&date=1998&atitle=A+study+of+prior+sensitivity+for+Bayesian+predictive+classificationbased+robust+speech+recognitionen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICASSP.1998.675371en_HK
dc.identifier.hkuros33653-

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