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Robust features for automatic estimation of physical parameters from speech
Estimating speaker's physical parameters like height, weight and shoulder size can assist in voice forensics by providing additional knowledge about the speaker. In this work, statistics of the components of background GMM are employed as features in estimating the physical parameters. These fe...
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| creator | Babu, Kalluri Shareef Vijayasenan, Deepu |
| description | Estimating speaker's physical parameters like height, weight and shoulder size can assist in voice forensics by providing additional knowledge about the speaker. In this work, statistics of the components of background GMM are employed as features in estimating the physical parameters. These features improved the performance of height and shoulder size estimation as compared to our earlier attempt based on a Bag of Word representation. The robustness of the features is validated using two different training subsets containing different languages. |
| doi_str_mv | 10.1109/TENCON.2017.8228097 |
| format | conference_proceeding |
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In this work, statistics of the components of background GMM are employed as features in estimating the physical parameters. These features improved the performance of height and shoulder size estimation as compared to our earlier attempt based on a Bag of Word representation. The robustness of the features is validated using two different training subsets containing different languages.</description><identifier>EISSN: 2159-3450</identifier><identifier>EISBN: 9781509011346</identifier><identifier>EISBN: 150901134X</identifier><identifier>DOI: 10.1109/TENCON.2017.8228097</identifier><language>eng</language><publisher>IEEE</publisher><subject>Estimation ; Feature extraction ; first order statistics ; GMM-UBM ; height ; MFCC ; Physical parameters ; Robustness ; shoulder size ; Speech ; Speech forensics ; Support vector machines ; SVR ; Training ; Training data ; weight</subject><ispartof>TENCON 2017 - 2017 IEEE Region 10 Conference, 2017, p.1515-1519</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/8228097$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8228097$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Babu, Kalluri Shareef</creatorcontrib><creatorcontrib>Vijayasenan, Deepu</creatorcontrib><title>Robust features for automatic estimation of physical parameters from speech</title><title>TENCON 2017 - 2017 IEEE Region 10 Conference</title><addtitle>TENCON</addtitle><description>Estimating speaker's physical parameters like height, weight and shoulder size can assist in voice forensics by providing additional knowledge about the speaker. In this work, statistics of the components of background GMM are employed as features in estimating the physical parameters. These features improved the performance of height and shoulder size estimation as compared to our earlier attempt based on a Bag of Word representation. The robustness of the features is validated using two different training subsets containing different languages.</description><subject>Estimation</subject><subject>Feature extraction</subject><subject>first order statistics</subject><subject>GMM-UBM</subject><subject>height</subject><subject>MFCC</subject><subject>Physical parameters</subject><subject>Robustness</subject><subject>shoulder size</subject><subject>Speech</subject><subject>Speech forensics</subject><subject>Support vector machines</subject><subject>SVR</subject><subject>Training</subject><subject>Training data</subject><subject>weight</subject><issn>2159-3450</issn><isbn>9781509011346</isbn><isbn>150901134X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2017</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotT9tqAjEUTAuFit0v8CU_sPYk2dwei9gLikKxz3I2nuAW7S5JfPDvu6XOywzDMMwwNhMwFwL88265WWw3cwnCzp2UDry9Y5W3TmjwIIRqzD2bSKF9rRoNj6zK-RtGGJDg7IStPvv2kguPhOWSKPPYJ46X0p-xdIFTLt2f6n94H_lwvOYu4IkPmPBMhdKYT_2Z54EoHJ_YQ8RTpurGU_b1utwt3uv19u1j8bKuO2F1qYNsdYNGBQsOKcpIB0fmQJqUQUMeA5FsQxOMCy2qoEBH64PGqDU0ozNls__ejoj2QxoXpuv-dl_9AvgdUTs</recordid><startdate>201711</startdate><enddate>201711</enddate><creator>Babu, Kalluri Shareef</creator><creator>Vijayasenan, Deepu</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201711</creationdate><title>Robust features for automatic estimation of physical parameters from speech</title><author>Babu, Kalluri Shareef ; Vijayasenan, Deepu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c2b54a63c708aef2fed8e6de5e36a6e9acee2bc4c68cba3c305f79c5af5504ba3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Estimation</topic><topic>Feature extraction</topic><topic>first order statistics</topic><topic>GMM-UBM</topic><topic>height</topic><topic>MFCC</topic><topic>Physical parameters</topic><topic>Robustness</topic><topic>shoulder size</topic><topic>Speech</topic><topic>Speech forensics</topic><topic>Support vector machines</topic><topic>SVR</topic><topic>Training</topic><topic>Training data</topic><topic>weight</topic><toplevel>online_resources</toplevel><creatorcontrib>Babu, Kalluri Shareef</creatorcontrib><creatorcontrib>Vijayasenan, Deepu</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>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>Babu, Kalluri Shareef</au><au>Vijayasenan, Deepu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Robust features for automatic estimation of physical parameters from speech</atitle><btitle>TENCON 2017 - 2017 IEEE Region 10 Conference</btitle><stitle>TENCON</stitle><date>2017-11</date><risdate>2017</risdate><spage>1515</spage><epage>1519</epage><pages>1515-1519</pages><eissn>2159-3450</eissn><eisbn>9781509011346</eisbn><eisbn>150901134X</eisbn><abstract>Estimating speaker's physical parameters like height, weight and shoulder size can assist in voice forensics by providing additional knowledge about the speaker. In this work, statistics of the components of background GMM are employed as features in estimating the physical parameters. These features improved the performance of height and shoulder size estimation as compared to our earlier attempt based on a Bag of Word representation. The robustness of the features is validated using two different training subsets containing different languages.</abstract><pub>IEEE</pub><doi>10.1109/TENCON.2017.8228097</doi><tpages>5</tpages></addata></record> |
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| identifier | EISSN: 2159-3450 |
| ispartof | TENCON 2017 - 2017 IEEE Region 10 Conference, 2017, p.1515-1519 |
| issn | 2159-3450 |
| language | eng |
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| source | IEEE Xplore All Conference Series |
| subjects | Estimation Feature extraction first order statistics GMM-UBM height MFCC Physical parameters Robustness shoulder size Speech Speech forensics Support vector machines SVR Training Training data weight |
| title | Robust features for automatic estimation of physical parameters from speech |
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