Audiovusual automatic speech segmentation
Audiovisual speech segmentation using visual information together with audio data is introduced. The collaboration of audio and visual data results in lower average absolute boundary error between the manual segmentation and automatic segmentation results that directly affects the quality of speech...
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creator | Akdemir, E. Ciloglu, T. |
description | Audiovisual speech segmentation using visual information together with audio data is introduced. The collaboration of audio and visual data results in lower average absolute boundary error between the manual segmentation and automatic segmentation results that directly affects the quality of speech processing systems using the segmented database. The audio and visual feature vectors are fused at the feature level and used in a HMM based speech segmentation system. A Turkish audiovisual speech database has been prepared and used in the experiments. The average absolute boundary error decreases up to 20.82% by using different audiovisual feature vectors. |
doi_str_mv | 10.1109/SIU.2011.5929796 |
format | Conference Proceeding |
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The collaboration of audio and visual data results in lower average absolute boundary error between the manual segmentation and automatic segmentation results that directly affects the quality of speech processing systems using the segmented database. The audio and visual feature vectors are fused at the feature level and used in a HMM based speech segmentation system. A Turkish audiovisual speech database has been prepared and used in the experiments. 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The collaboration of audio and visual data results in lower average absolute boundary error between the manual segmentation and automatic segmentation results that directly affects the quality of speech processing systems using the segmented database. The audio and visual feature vectors are fused at the feature level and used in a HMM based speech segmentation system. A Turkish audiovisual speech database has been prepared and used in the experiments. The average absolute boundary error decreases up to 20.82% by using different audiovisual feature vectors.</description><subject>Conferences</subject><subject>Hidden Markov models</subject><subject>Mel frequency cepstral coefficient</subject><subject>Speech</subject><subject>Speech processing</subject><subject>Visualization</subject><issn>2165-0608</issn><issn>2693-3616</issn><isbn>1457704625</isbn><isbn>9781457704628</isbn><isbn>1457704633</isbn><isbn>9781457704635</isbn><isbn>9781457704611</isbn><isbn>1457704617</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9zjsLwjAYheF4A6t2F1y6OrR-X9KkzSii6KzOJdSold5oGsF_b4eCm9OB91kOIUuEABHk5ny6BhQQAy6pjKQYkBmGPIogFIwNiUOFZD4TKEY_oHzcAQrug4B4SlxjXgCAIqZMUoest_aWVW9rrMo9ZduqUG2WeqbWOn16Rj8KXbZdqsoFmdxVbrTb75ysDvvL7uhnWuukbrJCNZ-kv8b-6xcNXDat</recordid><startdate>201104</startdate><enddate>201104</enddate><creator>Akdemir, E.</creator><creator>Ciloglu, T.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201104</creationdate><title>Audiovusual automatic speech segmentation</title><author>Akdemir, E. ; Ciloglu, T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_59297963</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Conferences</topic><topic>Hidden Markov models</topic><topic>Mel frequency cepstral coefficient</topic><topic>Speech</topic><topic>Speech processing</topic><topic>Visualization</topic><toplevel>online_resources</toplevel><creatorcontrib>Akdemir, E.</creatorcontrib><creatorcontrib>Ciloglu, T.</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>Akdemir, E.</au><au>Ciloglu, T.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Audiovusual automatic speech segmentation</atitle><btitle>2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)</btitle><stitle>SIU</stitle><date>2011-04</date><risdate>2011</risdate><spage>896</spage><epage>899</epage><pages>896-899</pages><issn>2165-0608</issn><eissn>2693-3616</eissn><isbn>1457704625</isbn><isbn>9781457704628</isbn><eisbn>1457704633</eisbn><eisbn>9781457704635</eisbn><eisbn>9781457704611</eisbn><eisbn>1457704617</eisbn><abstract>Audiovisual speech segmentation using visual information together with audio data is introduced. The collaboration of audio and visual data results in lower average absolute boundary error between the manual segmentation and automatic segmentation results that directly affects the quality of speech processing systems using the segmented database. The audio and visual feature vectors are fused at the feature level and used in a HMM based speech segmentation system. A Turkish audiovisual speech database has been prepared and used in the experiments. The average absolute boundary error decreases up to 20.82% by using different audiovisual feature vectors.</abstract><pub>IEEE</pub><doi>10.1109/SIU.2011.5929796</doi></addata></record> |
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ispartof | 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU), 2011, p.896-899 |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Conferences Hidden Markov models Mel frequency cepstral coefficient Speech Speech processing Visualization |
title | Audiovusual automatic speech segmentation |
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