A GMM supervector Kernel with the Bhattacharyya distance for SVM based speaker recognition
Gaussian mixture model (GMM) supervector is one of the effective techniques in text independent speaker recognition. In our previous work, we introduce the GMM-UBM mean interval (GUMI) concept based on the Bhattacharyya distance. Subsequently GUMI kernel was successfully used in conjunction with sup...
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| creator | Chang Huai You Kong Aik Lee Haizhou Li |
| description | Gaussian mixture model (GMM) supervector is one of the effective techniques in text independent speaker recognition. In our previous work, we introduce the GMM-UBM mean interval (GUMI) concept based on the Bhattacharyya distance. Subsequently GUMI kernel was successfully used in conjunction with support vector machine (SVM) for speaker recognition. Besides the first order statistics, it is generally believed that speaker cues are also partly conveyed by second order statistics. In this paper, we extend the Bhattacharyya-based SVM kernel by constructing the supervector with the mean statistical vector and the covariance statistical vector. Comparing with the Kullback-Leibler (KL) kernel, we demonstrate the effectiveness of the new kernel on the 2006 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) dataset. |
| doi_str_mv | 10.1109/ICASSP.2009.4960560 |
| format | Conference Proceeding |
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Comparing with the Kullback-Leibler (KL) kernel, we demonstrate the effectiveness of the new kernel on the 2006 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) dataset.</description><subject>Cost function</subject><subject>Distance measurement</subject><subject>Error analysis</subject><subject>Gaussian Mixture Model</subject><subject>Kernel</subject><subject>NIST</subject><subject>NIST Evaluation</subject><subject>Speaker recognition</subject><subject>Speaker Verification</subject><subject>Speech</subject><subject>Statistics</subject><subject>Supervector</subject><subject>Support Vector Machine</subject><subject>Support vector machines</subject><subject>Testing</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424423538</isbn><isbn>1424423538</isbn><isbn>9781424423545</isbn><isbn>1424423546</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVUM1OwzAYC38S1dgT7JIXaMl_m-OYYEOsAmmAEJcpS77SwOiqJID29lRiF3zxwZZlG6EJJQWlRF_ezqar1UPBCNGF0IpIRY7QWJcVFUwIxqWQxyhjvNQ51eTl5J_Gq1OUUclIrqjQ52gc4zsZICSnQmbodYrndY3jVw_hG2zaBXwHoYMt_vGpxakFfNWalIxtTdjvDXY-JtNZwM1gXT3XeGMiOBx7MB8QcAC7e-t88rvuAp01ZhthfOARerq5fpwt8uX9fNi0zD0tZcqtlg1nYJXcOMOE48zYRmnJnWu0qmjZKOeUcBVjQEtRkqpy1vINCGBcKcdHaPKX6wFg3Qf_OTRdH57ivyivWQM</recordid><startdate>200904</startdate><enddate>200904</enddate><creator>Chang Huai You</creator><creator>Kong Aik Lee</creator><creator>Haizhou Li</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200904</creationdate><title>A GMM supervector Kernel with the Bhattacharyya distance for SVM based speaker recognition</title><author>Chang Huai You ; Kong Aik Lee ; Haizhou Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c95f32ec65bda24d32acf6953ddf96817f6dd64d822e1747088dcc3be4e2366d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Cost function</topic><topic>Distance measurement</topic><topic>Error analysis</topic><topic>Gaussian Mixture Model</topic><topic>Kernel</topic><topic>NIST</topic><topic>NIST Evaluation</topic><topic>Speaker recognition</topic><topic>Speaker Verification</topic><topic>Speech</topic><topic>Statistics</topic><topic>Supervector</topic><topic>Support Vector Machine</topic><topic>Support vector machines</topic><topic>Testing</topic><toplevel>online_resources</toplevel><creatorcontrib>Chang Huai You</creatorcontrib><creatorcontrib>Kong Aik Lee</creatorcontrib><creatorcontrib>Haizhou Li</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>Chang Huai You</au><au>Kong Aik Lee</au><au>Haizhou Li</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A GMM supervector Kernel with the Bhattacharyya distance for SVM based speaker recognition</atitle><btitle>2009 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2009-04</date><risdate>2009</risdate><spage>4221</spage><epage>4224</epage><pages>4221-4224</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424423538</isbn><isbn>1424423538</isbn><eisbn>9781424423545</eisbn><eisbn>1424423546</eisbn><abstract>Gaussian mixture model (GMM) supervector is one of the effective techniques in text independent speaker recognition. In our previous work, we introduce the GMM-UBM mean interval (GUMI) concept based on the Bhattacharyya distance. Subsequently GUMI kernel was successfully used in conjunction with support vector machine (SVM) for speaker recognition. Besides the first order statistics, it is generally believed that speaker cues are also partly conveyed by second order statistics. In this paper, we extend the Bhattacharyya-based SVM kernel by constructing the supervector with the mean statistical vector and the covariance statistical vector. Comparing with the Kullback-Leibler (KL) kernel, we demonstrate the effectiveness of the new kernel on the 2006 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) dataset.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2009.4960560</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, p.4221-4224 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_4960560 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Cost function Distance measurement Error analysis Gaussian Mixture Model Kernel NIST NIST Evaluation Speaker recognition Speaker Verification Speech Statistics Supervector Support Vector Machine Support vector machines Testing |
| title | A GMM supervector Kernel with the Bhattacharyya distance for SVM based speaker recognition |
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