High-performance low-complexity wordspotting using neural networks
A high-performance low-complexity neural network wordspotter was developed using radial basis function (RBF) neutral networks in a hidden Markov model (HMM) framework. Two new complementary approaches substantially improve performance on the talker-independent Switchboard corpus. Figure of merit (FO...
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Veröffentlicht in: | IEEE transactions on signal processing 1997-11, Vol.45 (11), p.2864-2870 |
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container_title | IEEE transactions on signal processing |
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creator | Chang, E.I. Lippmann, R.P. |
description | A high-performance low-complexity neural network wordspotter was developed using radial basis function (RBF) neutral networks in a hidden Markov model (HMM) framework. Two new complementary approaches substantially improve performance on the talker-independent Switchboard corpus. Figure of merit (FOM) training adapts wordspotter parameters to directly improve the FOM performance metric, and voice transformations generate additional training examples by warping the spectra of training data to mimic across-talker vocal tract length variability. |
doi_str_mv | 10.1109/78.650114 |
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Two new complementary approaches substantially improve performance on the talker-independent Switchboard corpus. Figure of merit (FOM) training adapts wordspotter parameters to directly improve the FOM performance metric, and voice transformations generate additional training examples by warping the spectra of training data to mimic across-talker vocal tract length variability.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/78.650114</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Acoustic signal detection ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Connectionism. Neural networks ; Control systems ; Exact sciences and technology ; Hidden Markov models ; Maximum likelihood detection ; Measurement ; Neural networks ; NIST ; Speech ; Speech and sound recognition and synthesis. Linguistics ; Testing ; Training data</subject><ispartof>IEEE transactions on signal processing, 1997-11, Vol.45 (11), p.2864-2870</ispartof><rights>1998 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c266t-ed096e2479630da036ddb057100f6994333f255a156293a961f56f912d6c67953</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/650114$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/650114$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=2074618$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Chang, E.I.</creatorcontrib><creatorcontrib>Lippmann, R.P.</creatorcontrib><title>High-performance low-complexity wordspotting using neural networks</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>A high-performance low-complexity neural network wordspotter was developed using radial basis function (RBF) neutral networks in a hidden Markov model (HMM) framework. Two new complementary approaches substantially improve performance on the talker-independent Switchboard corpus. Figure of merit (FOM) training adapts wordspotter parameters to directly improve the FOM performance metric, and voice transformations generate additional training examples by warping the spectra of training data to mimic across-talker vocal tract length variability.</description><subject>Acoustic signal detection</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Control systems</subject><subject>Exact sciences and technology</subject><subject>Hidden Markov models</subject><subject>Maximum likelihood detection</subject><subject>Measurement</subject><subject>Neural networks</subject><subject>NIST</subject><subject>Speech</subject><subject>Speech and sound recognition and synthesis. Linguistics</subject><subject>Testing</subject><subject>Training data</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><recordid>eNo9kL1PwzAUxC0EEqUwsDJ1QEgMKc9fz_EIFVCkSiwgsUUmsUsgiYOdqPS_J1WqLu-edL-74Qi5pDCnFPSdSucogVJxRCZUC5qAUHg8_CB5IlP1cUrOYvwGoEJonJCHZbn-SlobnA-1aXI7q_wmyX3dVvav7LazjQ9FbH3Xlc161sfdbWwfTDVIN5g_8ZycOFNFe7HXKXl_enxbLJPV6_PL4n6V5AyxS2wBGi0TSiOHwgDHovgEqSiAQ60F59wxKQ2VyDQ3GqmT6DRlBeaotORTcjP2tsH_9jZ2WV3G3FaVaazvY8ZSzhmgHsDbEcyDjzFYl7WhrE3YZhSy3UqZSrNxpYG93peamJvKhWGDMh4CDJRAmg7Y1YiV1tqDu-_4B0Arbf0</recordid><startdate>19971101</startdate><enddate>19971101</enddate><creator>Chang, E.I.</creator><creator>Lippmann, R.P.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19971101</creationdate><title>High-performance low-complexity wordspotting using neural networks</title><author>Chang, E.I. ; Lippmann, R.P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c266t-ed096e2479630da036ddb057100f6994333f255a156293a961f56f912d6c67953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Acoustic signal detection</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Control systems</topic><topic>Exact sciences and technology</topic><topic>Hidden Markov models</topic><topic>Maximum likelihood detection</topic><topic>Measurement</topic><topic>Neural networks</topic><topic>NIST</topic><topic>Speech</topic><topic>Speech and sound recognition and synthesis. Linguistics</topic><topic>Testing</topic><topic>Training data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, E.I.</creatorcontrib><creatorcontrib>Lippmann, R.P.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chang, E.I.</au><au>Lippmann, R.P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>High-performance low-complexity wordspotting using neural networks</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>1997-11-01</date><risdate>1997</risdate><volume>45</volume><issue>11</issue><spage>2864</spage><epage>2870</epage><pages>2864-2870</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>A high-performance low-complexity neural network wordspotter was developed using radial basis function (RBF) neutral networks in a hidden Markov model (HMM) framework. Two new complementary approaches substantially improve performance on the talker-independent Switchboard corpus. Figure of merit (FOM) training adapts wordspotter parameters to directly improve the FOM performance metric, and voice transformations generate additional training examples by warping the spectra of training data to mimic across-talker vocal tract length variability.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/78.650114</doi><tpages>7</tpages></addata></record> |
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subjects | Acoustic signal detection Applied sciences Artificial intelligence Computer science control theory systems Connectionism. Neural networks Control systems Exact sciences and technology Hidden Markov models Maximum likelihood detection Measurement Neural networks NIST Speech Speech and sound recognition and synthesis. Linguistics Testing Training data |
title | High-performance low-complexity wordspotting using neural networks |
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