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Conference Paper: A Bayesian predictive classification approach to robust speech recognition
Title | A Bayesian predictive classification approach to robust speech recognition |
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Authors | |
Keywords | Engineering Electrical engineering |
Issue Date | 1997 |
Publisher | IEEE. |
Citation | IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Munich, Germany, 21-24 April 1997, v. 2, p. 1547-1550 How to Cite? |
Abstract | We introduce a new Bayesian predictive classification (BPC) approach to robust speech recognition and apply the BPC framework to Gaussian mixture continuous density hidden Markov model based speech recognition. We propose and focus on one of the approximate BPC approaches called quasi-Bayesian predictive classification (QBPC). In comparison with the standard plug-in maximum a posteriori decoding, when the QBPC method is applied to speaker independent recognition of a confusable vocabulary namely 26 English letters, where a broad range of mismatches between training and testing conditions exist, the QBPC achieves around 14% relative recognition error rate reduction. While the QBPC method is applied to cross-gender testing on a less confusable vocabulary, namely 20 English digits and commands, the QBPC method achieves around 24% relative recognition error rate reduction. |
Persistent Identifier | http://hdl.handle.net/10722/45586 |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Huo, Q | en_HK |
dc.contributor.author | Jiang, H | en_HK |
dc.contributor.author | Lee, CH | en_HK |
dc.date.accessioned | 2007-10-30T06:29:45Z | - |
dc.date.available | 2007-10-30T06:29:45Z | - |
dc.date.issued | 1997 | en_HK |
dc.identifier.citation | IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Munich, Germany, 21-24 April 1997, v. 2, p. 1547-1550 | en_HK |
dc.identifier.issn | 1520-6149 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/45586 | - |
dc.description.abstract | We introduce a new Bayesian predictive classification (BPC) approach to robust speech recognition and apply the BPC framework to Gaussian mixture continuous density hidden Markov model based speech recognition. We propose and focus on one of the approximate BPC approaches called quasi-Bayesian predictive classification (QBPC). In comparison with the standard plug-in maximum a posteriori decoding, when the QBPC method is applied to speaker independent recognition of a confusable vocabulary namely 26 English letters, where a broad range of mismatches between training and testing conditions exist, the QBPC achieves around 14% relative recognition error rate reduction. While the QBPC method is applied to cross-gender testing on a less confusable vocabulary, namely 20 English digits and commands, the QBPC method achieves around 24% relative recognition error rate reduction. | en_HK |
dc.format.extent | 483849 bytes | - |
dc.format.extent | 7254 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.format.mimetype | text/plain | - |
dc.language | eng | en_HK |
dc.publisher | IEEE. | en_HK |
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. | - |
dc.subject | Engineering | en_HK |
dc.subject | Electrical engineering | en_HK |
dc.title | A Bayesian predictive classification approach to robust 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=2&spage=1547&epage=1550&date=1997&atitle=A+Bayesian+predictive+classification+approach+to+robust+speech+recognition | en_HK |
dc.description.nature | published_or_final_version | en_HK |
dc.identifier.doi | 10.1109/ICASSP.1997.596246 | en_HK |
dc.identifier.hkuros | 31111 | - |
dc.identifier.issnl | 1520-6149 | - |