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Emotion detection using relative amplitude-based features through speech
Automatic speech recognition analysis has been an active part in computer science for more than two decades. In general, to detect an emotion, long continuous signal is needed. Relative amplitude reduces bias of glottal mutation of speech wave amplitude and obtains a normalized measure without conce...
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| container_end_page | 525 |
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| creator | Mohan Kudiri, Krishna Md Said, A. Nayan, M. Y. |
| description | Automatic speech recognition analysis has been an active part in computer science for more than two decades. In general, to detect an emotion, long continuous signal is needed. Relative amplitude reduces bias of glottal mutation of speech wave amplitude and obtains a normalized measure without concern of information from being distinct in feature. Nonverbal communication plays crucial role in human-human or human-machine interpersonal relationships. In this paper, we propose the use of relative bin frequency coefficients for speech signal segmentation. Here, the support vector machine classifier is used to implement automatic emotion detection system. |
| doi_str_mv | 10.1109/ICCISci.2012.6297301 |
| format | conference_proceeding |
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Y.</creator><creatorcontrib>Mohan Kudiri, Krishna ; Md Said, A. ; Nayan, M. Y.</creatorcontrib><description>Automatic speech recognition analysis has been an active part in computer science for more than two decades. In general, to detect an emotion, long continuous signal is needed. Relative amplitude reduces bias of glottal mutation of speech wave amplitude and obtains a normalized measure without concern of information from being distinct in feature. Nonverbal communication plays crucial role in human-human or human-machine interpersonal relationships. In this paper, we propose the use of relative bin frequency coefficients for speech signal segmentation. 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Y.</creatorcontrib><title>Emotion detection using relative amplitude-based features through speech</title><title>2012 International Conference on Computer & Information Science (ICCIS)</title><addtitle>ICCISci</addtitle><description>Automatic speech recognition analysis has been an active part in computer science for more than two decades. In general, to detect an emotion, long continuous signal is needed. Relative amplitude reduces bias of glottal mutation of speech wave amplitude and obtains a normalized measure without concern of information from being distinct in feature. Nonverbal communication plays crucial role in human-human or human-machine interpersonal relationships. In this paper, we propose the use of relative bin frequency coefficients for speech signal segmentation. Here, the support vector machine classifier is used to implement automatic emotion detection system.</description><subject>Artificial neural networks</subject><subject>Benchmark testing</subject><subject>Computational modeling</subject><subject>emotion detection</subject><subject>Hidden Markov models</subject><subject>Kernel</subject><subject>relative bin frequency features</subject><subject>relative sub-image based features</subject><subject>support vector machine</subject><subject>Support vector machines</subject><isbn>1467319376</isbn><isbn>9781467319379</isbn><isbn>1467319368</isbn><isbn>9781467319386</isbn><isbn>1467319384</isbn><isbn>9781467319362</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpFj8tKw0AYhUdEUGufQBfzAolzzcwsJVQbKLiw-zKXP81I0oTMRPDtLVrwbM75Ngc-hJ4oKSkl5rmp6-bDx5IRysqKGcUJvUL3VFSKU8Mrff0PqrpF65Q-yTlKU6LZHdpuhjHH8YQDZPC_a0nxdMQz9DbHL8B2mPqYlwCFswkCbsHmZYaEczePy7HDaQLw3QO6aW2fYH3pFdq_bvb1tti9vzX1y66IhuRC8FAZRoOQBjSTTipmhfNSyNY5BVbQcFbwQgTXMkmZ1UwbGkAy44UPnq_Q499tBIDDNMfBzt-Hizj_ATCiTlM</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>Mohan Kudiri, Krishna</creator><creator>Md Said, A.</creator><creator>Nayan, M. 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Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-43d6921d459e825b572a4bc545fbb7ea41d973c44dbf2512a82891de529c4cdc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Artificial neural networks</topic><topic>Benchmark testing</topic><topic>Computational modeling</topic><topic>emotion detection</topic><topic>Hidden Markov models</topic><topic>Kernel</topic><topic>relative bin frequency features</topic><topic>relative sub-image based features</topic><topic>support vector machine</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Mohan Kudiri, Krishna</creatorcontrib><creatorcontrib>Md Said, A.</creatorcontrib><creatorcontrib>Nayan, M. Y.</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 Xplore</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>Mohan Kudiri, Krishna</au><au>Md Said, A.</au><au>Nayan, M. Y.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Emotion detection using relative amplitude-based features through speech</atitle><btitle>2012 International Conference on Computer & Information Science (ICCIS)</btitle><stitle>ICCISci</stitle><date>2012-06</date><risdate>2012</risdate><volume>1</volume><spage>522</spage><epage>525</epage><pages>522-525</pages><isbn>1467319376</isbn><isbn>9781467319379</isbn><eisbn>1467319368</eisbn><eisbn>9781467319386</eisbn><eisbn>1467319384</eisbn><eisbn>9781467319362</eisbn><abstract>Automatic speech recognition analysis has been an active part in computer science for more than two decades. In general, to detect an emotion, long continuous signal is needed. Relative amplitude reduces bias of glottal mutation of speech wave amplitude and obtains a normalized measure without concern of information from being distinct in feature. Nonverbal communication plays crucial role in human-human or human-machine interpersonal relationships. In this paper, we propose the use of relative bin frequency coefficients for speech signal segmentation. Here, the support vector machine classifier is used to implement automatic emotion detection system.</abstract><pub>IEEE</pub><doi>10.1109/ICCISci.2012.6297301</doi><tpages>4</tpages></addata></record> |
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| identifier | ISBN: 1467319376 |
| ispartof | 2012 International Conference on Computer & Information Science (ICCIS), 2012, Vol.1, p.522-525 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Benchmark testing Computational modeling emotion detection Hidden Markov models Kernel relative bin frequency features relative sub-image based features support vector machine Support vector machines |
| title | Emotion detection using relative amplitude-based features through speech |
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