Maximum Entropy Confidence Estimation for Speech Recognition
For many automatic speech recognition (ASR) applications, it is useful to predict the likelihood that the recognized string contains an error. This paper explores two modifications of a classic design. First, it replaces the standard maximum likelihood classifier with a maximum entropy classifier. T...
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creator | White, C. Droppo, J. Acero, A. Odell, J. |
description | For many automatic speech recognition (ASR) applications, it is useful to predict the likelihood that the recognized string contains an error. This paper explores two modifications of a classic design. First, it replaces the standard maximum likelihood classifier with a maximum entropy classifier. The maximum entropy framework carries the dual advantages discriminative training and reasonable generalization. Second, it includes a number of alternative features. Our ASR system is heavily pruned, and often produces recognition lattices with only a single path. These alternate features are meant to serve as a surrogate for the typical features that can be computed from a rich lattice. We show that the maximum entropy classifier easily outperforms the standard baseline system, and the alternative features provide consistent gains for all of our test sets. |
doi_str_mv | 10.1109/ICASSP.2007.367036 |
format | Conference Proceeding |
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We show that the maximum entropy classifier easily outperforms the standard baseline system, and the alternative features provide consistent gains for all of our test sets.</description><subject>Automatic speech recognition</subject><subject>Engines</subject><subject>Entropy</subject><subject>Lattices</subject><subject>Maximum entropy methods</subject><subject>Maximum likelihood decoding</subject><subject>Maximum likelihood estimation</subject><subject>Natural languages</subject><subject>Speech processing</subject><subject>Speech recognition</subject><subject>System testing</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424407279</isbn><isbn>1424407273</isbn><isbn>9781424407286</isbn><isbn>1424407281</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVjttKxDAUReMNrOP8gL7kB1pPktOcBHyRoV5gRLEKvg1NJtWIvdBWcP7eGfTFp73ZCzaLsTMBmRBgL-4WV2X5mEkAypQmUHqPzS0ZgRIRSBq9zxKpyKbCwuvBP0b2kCUil5BqgfaYnYzjBwAYQpOwy_vqOzZfDS_aaej6DV90bR3XofWBF-MUm2qKXcvrbuBlH4J_50_Bd29t3M2n7KiuPscw_8sZe7kunhe36fLhZmu8TKOgfEqdtMavHRFo7dB7yNFXda1qcLsKQroKrFEO0BIpdCS1IeV0IEforJqx89_fGEJY9cPWatisUAojJaofSJ5M5g</recordid><startdate>200704</startdate><enddate>200704</enddate><creator>White, C.</creator><creator>Droppo, J.</creator><creator>Acero, A.</creator><creator>Odell, J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200704</creationdate><title>Maximum Entropy Confidence Estimation for Speech Recognition</title><author>White, C. ; Droppo, J. ; Acero, A. ; Odell, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-b298cdb77066b4cc054caff3f0b054c012ba0983b0497734b726873b6e7b74b93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Automatic speech recognition</topic><topic>Engines</topic><topic>Entropy</topic><topic>Lattices</topic><topic>Maximum entropy methods</topic><topic>Maximum likelihood decoding</topic><topic>Maximum likelihood estimation</topic><topic>Natural languages</topic><topic>Speech processing</topic><topic>Speech recognition</topic><topic>System testing</topic><toplevel>online_resources</toplevel><creatorcontrib>White, C.</creatorcontrib><creatorcontrib>Droppo, J.</creatorcontrib><creatorcontrib>Acero, A.</creatorcontrib><creatorcontrib>Odell, J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>White, C.</au><au>Droppo, J.</au><au>Acero, A.</au><au>Odell, J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Maximum Entropy Confidence Estimation for Speech Recognition</atitle><btitle>2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07</btitle><stitle>ICASSP</stitle><date>2007-04</date><risdate>2007</risdate><volume>4</volume><spage>IV-809</spage><epage>IV-812</epage><pages>IV-809-IV-812</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424407279</isbn><isbn>1424407273</isbn><eisbn>9781424407286</eisbn><eisbn>1424407281</eisbn><abstract>For many automatic speech recognition (ASR) applications, it is useful to predict the likelihood that the recognized string contains an error. This paper explores two modifications of a classic design. First, it replaces the standard maximum likelihood classifier with a maximum entropy classifier. The maximum entropy framework carries the dual advantages discriminative training and reasonable generalization. Second, it includes a number of alternative features. Our ASR system is heavily pruned, and often produces recognition lattices with only a single path. These alternate features are meant to serve as a surrogate for the typical features that can be computed from a rich lattice. We show that the maximum entropy classifier easily outperforms the standard baseline system, and the alternative features provide consistent gains for all of our test sets.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2007.367036</doi></addata></record> |
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subjects | Automatic speech recognition Engines Entropy Lattices Maximum entropy methods Maximum likelihood decoding Maximum likelihood estimation Natural languages Speech processing Speech recognition System testing |
title | Maximum Entropy Confidence Estimation for Speech Recognition |
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