Employing Fujisaki’s Intonation Model Parameters for Emotion Recognition
In this paper we are introducing the employment of features extracted from Fujisaki’s parameterization of pitch contour for the task of emotion recognition from speech. In evaluating the proposed features we have trained a decision tree inducer as well as the instance based learning algorithm. The d...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 453 |
|---|---|
| container_issue | |
| container_start_page | 443 |
| container_title | |
| container_volume | |
| creator | Zervas, Panagiotis Mporas, Iosif Fakotakis, Nikos Kokkinakis, George |
| description | In this paper we are introducing the employment of features extracted from Fujisaki’s parameterization of pitch contour for the task of emotion recognition from speech. In evaluating the proposed features we have trained a decision tree inducer as well as the instance based learning algorithm. The datasets utilized for training the classification models, were extracted from two emotional speech databases. Fujisaki’s parameters benefited all prediction models with an average raise of 9,52% in the total accuracy. |
| doi_str_mv | 10.1007/11752912_44 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>pascalfrancis_sprin</sourceid><recordid>TN_cdi_pascalfrancis_primary_19152094</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>19152094</sourcerecordid><originalsourceid>FETCH-LOGICAL-p219t-1bb26183113c9b712854906abf08da958df97ebd041987bd260a16258d07fcf53</originalsourceid><addsrcrecordid>eNpNkL9OwzAYxM0_ibZ04gWyMDAEvs92YntEVQtFRSAEs2UnTpU2iaM4HbrxGrweT0JLQWK60_1ONxwhlwg3CCBuEUVCFVLN-REZsoQD44hSHpMBpogxY1ydkLES8o8JeUoGwIDGSnB2ToYhrACACkUH5HFat5Xfls0ymm1WZTDr8uvjM0TzpveN6UvfRE8-d1X0YjpTu951ISp8F01r_wNfXeaXTbn3F-SsMFVw418dkffZ9G3yEC-e7-eTu0XcUlR9jNbSFCVDZJmyAqlMuILU2AJkblQi80IJZ3PgqKSwOU3BYEp3OYgiKxI2IleH3daEzFRFZ5qsDLrtytp0W40KEwqK73rXh17YoWbpOm29XweNoPdP6n9Psm_2kmEC</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Employing Fujisaki’s Intonation Model Parameters for Emotion Recognition</title><source>Springer Books</source><creator>Zervas, Panagiotis ; Mporas, Iosif ; Fakotakis, Nikos ; Kokkinakis, George</creator><contributor>Antoniou, Grigoris ; Plexousakis, Dimitris ; Potamias, George ; Spyropoulos, Costas</contributor><creatorcontrib>Zervas, Panagiotis ; Mporas, Iosif ; Fakotakis, Nikos ; Kokkinakis, George ; Antoniou, Grigoris ; Plexousakis, Dimitris ; Potamias, George ; Spyropoulos, Costas</creatorcontrib><description>In this paper we are introducing the employment of features extracted from Fujisaki’s parameterization of pitch contour for the task of emotion recognition from speech. In evaluating the proposed features we have trained a decision tree inducer as well as the instance based learning algorithm. The datasets utilized for training the classification models, were extracted from two emotional speech databases. Fujisaki’s parameters benefited all prediction models with an average raise of 9,52% in the total accuracy.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540341178</identifier><identifier>ISBN: 354034117X</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540341188</identifier><identifier>EISBN: 9783540341185</identifier><identifier>DOI: 10.1007/11752912_44</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Emotion Category ; Emotion Recognition ; Emotional Speech ; Exact sciences and technology ; Pitch Contour ; Total Accuracy</subject><ispartof>Advances in Artificial Intelligence, 2006, p.443-453</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><rights>2007 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11752912_44$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11752912_44$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>310,311,780,781,785,790,791,794,4051,4052,27930,38260,41447,42516</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19152094$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Antoniou, Grigoris</contributor><contributor>Plexousakis, Dimitris</contributor><contributor>Potamias, George</contributor><contributor>Spyropoulos, Costas</contributor><creatorcontrib>Zervas, Panagiotis</creatorcontrib><creatorcontrib>Mporas, Iosif</creatorcontrib><creatorcontrib>Fakotakis, Nikos</creatorcontrib><creatorcontrib>Kokkinakis, George</creatorcontrib><title>Employing Fujisaki’s Intonation Model Parameters for Emotion Recognition</title><title>Advances in Artificial Intelligence</title><description>In this paper we are introducing the employment of features extracted from Fujisaki’s parameterization of pitch contour for the task of emotion recognition from speech. In evaluating the proposed features we have trained a decision tree inducer as well as the instance based learning algorithm. The datasets utilized for training the classification models, were extracted from two emotional speech databases. Fujisaki’s parameters benefited all prediction models with an average raise of 9,52% in the total accuracy.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Emotion Category</subject><subject>Emotion Recognition</subject><subject>Emotional Speech</subject><subject>Exact sciences and technology</subject><subject>Pitch Contour</subject><subject>Total Accuracy</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540341178</isbn><isbn>354034117X</isbn><isbn>3540341188</isbn><isbn>9783540341185</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkL9OwzAYxM0_ibZ04gWyMDAEvs92YntEVQtFRSAEs2UnTpU2iaM4HbrxGrweT0JLQWK60_1ONxwhlwg3CCBuEUVCFVLN-REZsoQD44hSHpMBpogxY1ydkLES8o8JeUoGwIDGSnB2ToYhrACACkUH5HFat5Xfls0ymm1WZTDr8uvjM0TzpveN6UvfRE8-d1X0YjpTu951ISp8F01r_wNfXeaXTbn3F-SsMFVw418dkffZ9G3yEC-e7-eTu0XcUlR9jNbSFCVDZJmyAqlMuILU2AJkblQi80IJZ3PgqKSwOU3BYEp3OYgiKxI2IleH3daEzFRFZ5qsDLrtytp0W40KEwqK73rXh17YoWbpOm29XweNoPdP6n9Psm_2kmEC</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Zervas, Panagiotis</creator><creator>Mporas, Iosif</creator><creator>Fakotakis, Nikos</creator><creator>Kokkinakis, George</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Employing Fujisaki’s Intonation Model Parameters for Emotion Recognition</title><author>Zervas, Panagiotis ; Mporas, Iosif ; Fakotakis, Nikos ; Kokkinakis, George</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-1bb26183113c9b712854906abf08da958df97ebd041987bd260a16258d07fcf53</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Emotion Category</topic><topic>Emotion Recognition</topic><topic>Emotional Speech</topic><topic>Exact sciences and technology</topic><topic>Pitch Contour</topic><topic>Total Accuracy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zervas, Panagiotis</creatorcontrib><creatorcontrib>Mporas, Iosif</creatorcontrib><creatorcontrib>Fakotakis, Nikos</creatorcontrib><creatorcontrib>Kokkinakis, George</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zervas, Panagiotis</au><au>Mporas, Iosif</au><au>Fakotakis, Nikos</au><au>Kokkinakis, George</au><au>Antoniou, Grigoris</au><au>Plexousakis, Dimitris</au><au>Potamias, George</au><au>Spyropoulos, Costas</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Employing Fujisaki’s Intonation Model Parameters for Emotion Recognition</atitle><btitle>Advances in Artificial Intelligence</btitle><date>2006</date><risdate>2006</risdate><spage>443</spage><epage>453</epage><pages>443-453</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540341178</isbn><isbn>354034117X</isbn><eisbn>3540341188</eisbn><eisbn>9783540341185</eisbn><abstract>In this paper we are introducing the employment of features extracted from Fujisaki’s parameterization of pitch contour for the task of emotion recognition from speech. In evaluating the proposed features we have trained a decision tree inducer as well as the instance based learning algorithm. The datasets utilized for training the classification models, were extracted from two emotional speech databases. Fujisaki’s parameters benefited all prediction models with an average raise of 9,52% in the total accuracy.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11752912_44</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Artificial Intelligence, 2006, p.443-453 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19152094 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Emotion Category Emotion Recognition Emotional Speech Exact sciences and technology Pitch Contour Total Accuracy |
| title | Employing Fujisaki’s Intonation Model Parameters for Emotion Recognition |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-16T12%3A34%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Employing%20Fujisaki%E2%80%99s%20Intonation%20Model%20Parameters%20for%20Emotion%20Recognition&rft.btitle=Advances%20in%20Artificial%20Intelligence&rft.au=Zervas,%20Panagiotis&rft.date=2006&rft.spage=443&rft.epage=453&rft.pages=443-453&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=9783540341178&rft.isbn_list=354034117X&rft_id=info:doi/10.1007/11752912_44&rft_dat=%3Cpascalfrancis_sprin%3E19152094%3C/pascalfrancis_sprin%3E%3Curl%3E%3C/url%3E&rft.eisbn=3540341188&rft.eisbn_list=9783540341185&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |