Probabilistic optimum filtering for robust speech recognition
We present a new mapping algorithm for speech recognition that relates the features of simultaneous recordings of clean and noisy speech. The model is a piecewise linear transformation applied to the noisy speech feature. The transformation is a set of multidimensional linear least-squares filters w...
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| container_end_page | I/420 vol.1 |
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| creator | Neumeyer, L. Weintraub, M. |
| description | We present a new mapping algorithm for speech recognition that relates the features of simultaneous recordings of clean and noisy speech. The model is a piecewise linear transformation applied to the noisy speech feature. The transformation is a set of multidimensional linear least-squares filters whose outputs are combined using a conditional Gaussian model. The algorithm was tested using SRI's DECIPHER speech recognition system. Experimental results show how the mapping is used to reduce recognition errors when the training and testing acoustic environments do not match.< > |
| doi_str_mv | 10.1109/ICASSP.1994.389267 |
| format | Conference Proceeding |
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Experimental results show how the mapping is used to reduce recognition errors when the training and testing acoustic environments do not match.< ></description><identifier>ISSN: 1520-6149</identifier><identifier>ISBN: 0780317750</identifier><identifier>ISBN: 9780780317758</identifier><identifier>EISSN: 2379-190X</identifier><identifier>DOI: 10.1109/ICASSP.1994.389267</identifier><language>eng</language><publisher>IEEE</publisher><subject>Acoustic noise ; Acoustic testing ; Filtering ; Multidimensional systems ; Nonlinear filters ; Piecewise linear techniques ; Robustness ; Simultaneous localization and mapping ; Speech recognition ; Working environment noise</subject><ispartof>Proceedings of ICASSP '94. 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IEEE International Conference on Acoustics, Speech and Signal Processing</title><addtitle>ICASSP</addtitle><description>We present a new mapping algorithm for speech recognition that relates the features of simultaneous recordings of clean and noisy speech. The model is a piecewise linear transformation applied to the noisy speech feature. The transformation is a set of multidimensional linear least-squares filters whose outputs are combined using a conditional Gaussian model. The algorithm was tested using SRI's DECIPHER speech recognition system. Experimental results show how the mapping is used to reduce recognition errors when the training and testing acoustic environments do not match.< ></description><subject>Acoustic noise</subject><subject>Acoustic testing</subject><subject>Filtering</subject><subject>Multidimensional systems</subject><subject>Nonlinear filters</subject><subject>Piecewise linear techniques</subject><subject>Robustness</subject><subject>Simultaneous localization and mapping</subject><subject>Speech recognition</subject><subject>Working environment noise</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>0780317750</isbn><isbn>9780780317758</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1994</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj7lqAzEURUUWyMTxD7iaH5iJnpaRVKQIJhsYYrCLdEYaPzkKsyHJRf4-A85tbnO4nEvICmgNQM3jx_p5t9vWYIyouTasUVekYFyZCgz9uib3VGnKQSlJb0gBktGqAWHuyDKlHzpHSClAFORpG0dnXehCyqEtxymH_tyXPnQZYxhOpR9jOSPnlMs0IbbfZcR2PA0hh3F4ILfedgmX_70g-9eX_fq92ny-zYqbKmiVK7DaotCOSQ7QOCmEpQjmiC2XAqXw4ghMO-mNkBSYamhjvTbWc9c6g5wvyOoyGxDxMMXQ2_h7uPzmf2PMSxg</recordid><startdate>1994</startdate><enddate>1994</enddate><creator>Neumeyer, L.</creator><creator>Weintraub, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1994</creationdate><title>Probabilistic optimum filtering for robust speech recognition</title><author>Neumeyer, L. ; Weintraub, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i87t-1a8ae48b253116b544a0e19dec354e54f4d128b5f9450127606af89af3bcb9e33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1994</creationdate><topic>Acoustic noise</topic><topic>Acoustic testing</topic><topic>Filtering</topic><topic>Multidimensional systems</topic><topic>Nonlinear filters</topic><topic>Piecewise linear techniques</topic><topic>Robustness</topic><topic>Simultaneous localization and mapping</topic><topic>Speech recognition</topic><topic>Working environment noise</topic><toplevel>online_resources</toplevel><creatorcontrib>Neumeyer, L.</creatorcontrib><creatorcontrib>Weintraub, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Neumeyer, L.</au><au>Weintraub, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Probabilistic optimum filtering for robust speech recognition</atitle><btitle>Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>1994</date><risdate>1994</risdate><volume>i</volume><spage>I/417</spage><epage>I/420 vol.1</epage><pages>I/417-I/420 vol.1</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>0780317750</isbn><isbn>9780780317758</isbn><abstract>We present a new mapping algorithm for speech recognition that relates the features of simultaneous recordings of clean and noisy speech. The model is a piecewise linear transformation applied to the noisy speech feature. The transformation is a set of multidimensional linear least-squares filters whose outputs are combined using a conditional Gaussian model. The algorithm was tested using SRI's DECIPHER speech recognition system. Experimental results show how the mapping is used to reduce recognition errors when the training and testing acoustic environments do not match.< ></abstract><pub>IEEE</pub><doi>10.1109/ICASSP.1994.389267</doi></addata></record> |
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| identifier | ISSN: 1520-6149 |
| ispartof | Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing, 1994, Vol.i, p.I/417-I/420 vol.1 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_389267 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Acoustic noise Acoustic testing Filtering Multidimensional systems Nonlinear filters Piecewise linear techniques Robustness Simultaneous localization and mapping Speech recognition Working environment noise |
| title | Probabilistic optimum filtering for robust speech recognition |
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