Use of fuzzy min-max neural network for speaker identification
This paper presents the use of fuzzy min-max neural network for the text independent speaker identification. The fuzzy min-max neural network utilizes fuzzy sets as pattern classes. It is a three layer feedforward network that grows adaptively to meet the demands of the problem. The database contain...
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| creator | Jawarkar, N. P. Holambe, R. S. Basu, T. K. |
| description | This paper presents the use of fuzzy min-max neural network for the text independent speaker identification. The fuzzy min-max neural network utilizes fuzzy sets as pattern classes. It is a three layer feedforward network that grows adaptively to meet the demands of the problem. The database containing speech utterances recorded from fifty speakers in Marathi language is used for experimentation. Mel frequency cepstral coefficients that represent short time spectrum are used as features for identification. The results obtained with fuzzy min-max neural network are compared with Gaussian mixture model. It is observed that fuzzy neural network outperforms the Gaussian mixture model and attains the identification accuracy of 99.99% with 15 second speech utterance. |
| doi_str_mv | 10.1109/ICRTIT.2011.5972455 |
| format | Conference Proceeding |
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P. ; Holambe, R. S. ; Basu, T. K.</creator><creatorcontrib>Jawarkar, N. P. ; Holambe, R. S. ; Basu, T. K.</creatorcontrib><description>This paper presents the use of fuzzy min-max neural network for the text independent speaker identification. The fuzzy min-max neural network utilizes fuzzy sets as pattern classes. It is a three layer feedforward network that grows adaptively to meet the demands of the problem. The database containing speech utterances recorded from fifty speakers in Marathi language is used for experimentation. Mel frequency cepstral coefficients that represent short time spectrum are used as features for identification. The results obtained with fuzzy min-max neural network are compared with Gaussian mixture model. It is observed that fuzzy neural network outperforms the Gaussian mixture model and attains the identification accuracy of 99.99% with 15 second speech utterance.</description><identifier>ISBN: 9781457705885</identifier><identifier>ISBN: 1457705885</identifier><identifier>EISBN: 1457705893</identifier><identifier>EISBN: 1457705907</identifier><identifier>EISBN: 9781457705908</identifier><identifier>EISBN: 9781457705892</identifier><identifier>DOI: 10.1109/ICRTIT.2011.5972455</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Cepstral analysis ; classification ; Feature extraction ; fuzzy neural networks ; Hidden Markov models ; MFCC ; speaker identification ; Speaker recognition ; Speech ; Speech processing</subject><ispartof>2011 International Conference on Recent Trends in Information Technology (ICRTIT), 2011, p.178-182</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5972455$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54918</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5972455$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jawarkar, N. P.</creatorcontrib><creatorcontrib>Holambe, R. S.</creatorcontrib><creatorcontrib>Basu, T. K.</creatorcontrib><title>Use of fuzzy min-max neural network for speaker identification</title><title>2011 International Conference on Recent Trends in Information Technology (ICRTIT)</title><addtitle>ICRTIT</addtitle><description>This paper presents the use of fuzzy min-max neural network for the text independent speaker identification. The fuzzy min-max neural network utilizes fuzzy sets as pattern classes. It is a three layer feedforward network that grows adaptively to meet the demands of the problem. The database containing speech utterances recorded from fifty speakers in Marathi language is used for experimentation. Mel frequency cepstral coefficients that represent short time spectrum are used as features for identification. The results obtained with fuzzy min-max neural network are compared with Gaussian mixture model. It is observed that fuzzy neural network outperforms the Gaussian mixture model and attains the identification accuracy of 99.99% with 15 second speech utterance.</description><subject>Artificial neural networks</subject><subject>Cepstral analysis</subject><subject>classification</subject><subject>Feature extraction</subject><subject>fuzzy neural networks</subject><subject>Hidden Markov models</subject><subject>MFCC</subject><subject>speaker identification</subject><subject>Speaker recognition</subject><subject>Speech</subject><subject>Speech processing</subject><isbn>9781457705885</isbn><isbn>1457705885</isbn><isbn>1457705893</isbn><isbn>1457705907</isbn><isbn>9781457705908</isbn><isbn>9781457705892</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j81Kw0AUhUekoLZ5gm7mBRLnNzOzESSoDRQEietyk9yBsU1SJinaPr0B61mcj29z4BCy5izjnLnHsvioyioTjPNMOyOU1jfkgSttDNPWyVuSOGP_3eo7kozjF5uT5y4X4p48fY5IB0_96XI50y70aQc_tMdThMOM6XuIe-qHSMcjwh4jDS32U_ChgSkM_YosPBxGTK5ckur1pSo26fb9rSyet2lwbEp9DUZbxBYMMsOV4V4JaQ1a3yiLTLq60a3TumV5jVIrBWCNtcKwucHLJVn_zQZE3B1j6CCed9fH8he4AUpb</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Jawarkar, N. P.</creator><creator>Holambe, R. S.</creator><creator>Basu, T. K.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201106</creationdate><title>Use of fuzzy min-max neural network for speaker identification</title><author>Jawarkar, N. P. ; Holambe, R. S. ; Basu, T. K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-fba758eeda7e071471f42387e8fc48e039bc5d955d06be3544aa8788270788af3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Artificial neural networks</topic><topic>Cepstral analysis</topic><topic>classification</topic><topic>Feature extraction</topic><topic>fuzzy neural networks</topic><topic>Hidden Markov models</topic><topic>MFCC</topic><topic>speaker identification</topic><topic>Speaker recognition</topic><topic>Speech</topic><topic>Speech processing</topic><toplevel>online_resources</toplevel><creatorcontrib>Jawarkar, N. P.</creatorcontrib><creatorcontrib>Holambe, R. S.</creatorcontrib><creatorcontrib>Basu, T. K.</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>Jawarkar, N. P.</au><au>Holambe, R. S.</au><au>Basu, T. K.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Use of fuzzy min-max neural network for speaker identification</atitle><btitle>2011 International Conference on Recent Trends in Information Technology (ICRTIT)</btitle><stitle>ICRTIT</stitle><date>2011-06</date><risdate>2011</risdate><spage>178</spage><epage>182</epage><pages>178-182</pages><isbn>9781457705885</isbn><isbn>1457705885</isbn><eisbn>1457705893</eisbn><eisbn>1457705907</eisbn><eisbn>9781457705908</eisbn><eisbn>9781457705892</eisbn><abstract>This paper presents the use of fuzzy min-max neural network for the text independent speaker identification. The fuzzy min-max neural network utilizes fuzzy sets as pattern classes. It is a three layer feedforward network that grows adaptively to meet the demands of the problem. The database containing speech utterances recorded from fifty speakers in Marathi language is used for experimentation. Mel frequency cepstral coefficients that represent short time spectrum are used as features for identification. The results obtained with fuzzy min-max neural network are compared with Gaussian mixture model. It is observed that fuzzy neural network outperforms the Gaussian mixture model and attains the identification accuracy of 99.99% with 15 second speech utterance.</abstract><pub>IEEE</pub><doi>10.1109/ICRTIT.2011.5972455</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Cepstral analysis classification Feature extraction fuzzy neural networks Hidden Markov models MFCC speaker identification Speaker recognition Speech Speech processing |
| title | Use of fuzzy min-max neural network for speaker identification |
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