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Conference Paper: On-line Bayes adaptation of SCHMM parameters for speech recognition
Title | On-line Bayes adaptation of SCHMM parameters for speech recognition |
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Authors | |
Keywords | Engineering Electrical engineering |
Issue Date | 1995 |
Publisher | IEEE. |
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? |
Abstract | On-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 Identifier | http://hdl.handle.net/10722/45620 |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Huo, Q | en_HK |
dc.contributor.author | Chan, C | en_HK |
dc.date.accessioned | 2007-10-30T06:30:27Z | - |
dc.date.available | 2007-10-30T06:30:27Z | - |
dc.date.issued | 1995 | en_HK |
dc.identifier.citation | IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Detroit, MI, USA, 9-12 May 1995, v. 1, p. 708-711 | en_HK |
dc.identifier.issn | 1520-6149 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/45620 | - |
dc.description.abstract | On-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.extent | 392347 bytes | - |
dc.format.extent | 3669 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.format.mimetype | text/plain | - |
dc.language | eng | en_HK |
dc.publisher | IEEE. | en_HK |
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. | - |
dc.subject | Engineering | en_HK |
dc.subject | Electrical engineering | en_HK |
dc.title | On-line Bayes adaptation of SCHMM parameters for speech recognition | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.openurl | http://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+recognition | en_HK |
dc.description.nature | published_or_final_version | en_HK |
dc.identifier.doi | 10.1109/ICASSP.1995.479792 | en_HK |
dc.identifier.hkuros | 531 | - |
dc.identifier.issnl | 1520-6149 | - |