Multi-class SVM optimization using MCE training with application to topic identification

This paper presents a minimum classification error (MCE) training approach for improving the accuracy of multi-class support vector machine (SVM) classifiers. We have applied this approach to topic identification (topic ID) for human-human telephone conversations from the Fisher corpus using ASR lat...

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1. Verfasser:	Hazen, T J
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Automatic speech recognition Calibration Kernel Laboratories Lattices MCE training Natural languages Support vector machine classification Support vector machines SVM classifiers Telephony topic identification Virtual manufacturing
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creator	Hazen, T J
description	This paper presents a minimum classification error (MCE) training approach for improving the accuracy of multi-class support vector machine (SVM) classifiers. We have applied this approach to topic identification (topic ID) for human-human telephone conversations from the Fisher corpus using ASR lattice output. The new approach yields improved performance over the traditional techniques for training multi-class SVM classifiers on this task.
doi_str_mv	10.1109/ICASSP.2010.5494948
format	Conference Proceeding
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The new approach yields improved performance over the traditional techniques for training multi-class SVM classifiers on this task.</description><subject>Automatic speech recognition</subject><subject>Calibration</subject><subject>Kernel</subject><subject>Laboratories</subject><subject>Lattices</subject><subject>MCE training</subject><subject>Natural languages</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>SVM classifiers</subject><subject>Telephony</subject><subject>topic identification</subject><subject>Virtual manufacturing</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424442959</isbn><isbn>1424442958</isbn><isbn>9781424442966</isbn><isbn>1424442966</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVUNtKw0AQXW9grP2CvuwPpO7sJdl9lFAv0KAQlb6VzWajI2kaki2iX2-keZEZGM45w3DmELIAtgRg5uYxuy2K5yVnI6GkGUufkLlJNUgupeQmSU5JxEVqYjBsc_ZPU-acRKA4ixOQ5pJcDcMnY0ynUkdkkx-agLFr7DDQ4i2n-y7gDn9swH1LDwO27zTPVjT0Fts_8IXhg9qua9Add8J-7A4dxcq3AeuJvyYXtW0GP5_mjLzerV6yh3j9dD--s44RUhXi0WfJlRZOa14JDbUREoRiXrmqkqaUApjypXCmYklaS3B1pZz3Cri0xlkxI4vjXfTeb7sed7b_3k4hiV8DwVf6</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Hazen, T J</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201003</creationdate><title>Multi-class SVM optimization using MCE training with application to topic identification</title><author>Hazen, T J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-978b2583c882d381f9341350e5cdd49b43105eb3c9d067f41cfd5cee5124a9ca3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Automatic speech recognition</topic><topic>Calibration</topic><topic>Kernel</topic><topic>Laboratories</topic><topic>Lattices</topic><topic>MCE training</topic><topic>Natural languages</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>SVM classifiers</topic><topic>Telephony</topic><topic>topic identification</topic><topic>Virtual manufacturing</topic><toplevel>online_resources</toplevel><creatorcontrib>Hazen, T 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 Xplore</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hazen, T J</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multi-class SVM optimization using MCE training with application to topic identification</atitle><btitle>2010 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2010-03</date><risdate>2010</risdate><spage>5350</spage><epage>5353</epage><pages>5350-5353</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424442959</isbn><isbn>1424442958</isbn><eisbn>9781424442966</eisbn><eisbn>1424442966</eisbn><abstract>This paper presents a minimum classification error (MCE) training approach for improving the accuracy of multi-class support vector machine (SVM) classifiers. We have applied this approach to topic identification (topic ID) for human-human telephone conversations from the Fisher corpus using ASR lattice output. The new approach yields improved performance over the traditional techniques for training multi-class SVM classifiers on this task.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2010.5494948</doi><tpages>4</tpages></addata></record>
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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Automatic speech recognition Calibration Kernel Laboratories Lattices MCE training Natural languages Support vector machine classification Support vector machines SVM classifiers Telephony topic identification Virtual manufacturing
title	Multi-class SVM optimization using MCE training with application to topic identification
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