About: Recognition of Emotions in German Speech Using Gaussian Mixture Models

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	The contribution describes experiments with recognition of emotions in German speech signal based oil the same principle as recognition of speakers. The most robust algorithm for speaker recognition is based On Gaussian Mixture Models (GMM). We examine three parameter Sets: the first contains suprasegmental features, in the second are segmental features and the last is a combination of the two previous parameter sets. Further we want to explore the dependency of the classification accuracy Oil the number of GMM model components. The aim of this contribution is a recommendation the number of GMM components and the optimal selection of speech parameters for emotion recognition in German speech. The contribution describes experiments with recognition of emotions in German speech signal based oil the same principle as recognition of speakers. The most robust algorithm for speaker recognition is based On Gaussian Mixture Models (GMM). We examine three parameter Sets: the first contains suprasegmental features, in the second are segmental features and the last is a combination of the two previous parameter sets. Further we want to explore the dependency of the classification accuracy Oil the number of GMM model components. The aim of this contribution is a recommendation the number of GMM components and the optimal selection of speech parameters for emotion recognition in German speech. (en)
Title	Recognition of Emotions in German Speech Using Gaussian Mixture Models Recognition of Emotions in German Speech Using Gaussian Mixture Models (en)
skos:prefLabel	Recognition of Emotions in German Speech Using Gaussian Mixture Models Recognition of Emotions in German Speech Using Gaussian Mixture Models (en)
skos:notation	RIV/67985882:_____/09:00356050!RIV11-MSM-67985882
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(OC08010), Z(AV0Z20670512)
http://linked.open...vai/riv/dodaniDat	2011
http://linked.open...aciTvurceVysledku	Vích, Robert Vondra, Martin
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav fotoniky a elektroniky AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	338372
http://linked.open...ai/riv/idVysledku	RIV/67985882:_____/09:00356050
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	emotion recognition; speech emotions (en)
http://linked.open.../riv/klicoveSlovo	emotion recognition speech emotions
http://linked.open...ontrolniKodProRIV	[E915C7FCB1A3]
http://linked.open...v/mistoKonaniAkce	Vietri sul Mare
http://linked.open...i/riv/mistoVydani	Berlin
http://linked.open...i/riv/nazevZdroje	MULTIMODAL SIGNAL: COGNITIVE AND ALGORITHMIC ISSUES
http://linked.open...in/vavai/riv/obor	JA
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Emotional speech style analysis, modeling and synthesis
http://linked.open...UplatneniVysledku	2009
http://linked.open...iv/tvurceVysledku	Vondra, Martin Vích, Robert
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open...ain/vavai/riv/wos	000265464200026
http://linked.open.../riv/zahajeniAkce	2008-04-21 (xsd:date)
http://linked.open...n/vavai/riv/zamer	Materiály, struktury, systémy a signály v elektronice, optoelektronice a fotonice
number of pages	8 (xsd:int)
http://purl.org/ne...btex#hasPublisher	SPRINGER-VERLAG
https://schema.org/isbn	978-3-642-00524-4
is http://linked.open...avai/riv/vysledek of	Recognition of Emotions in German Speech Using Gaussian Mixture Models Recognition of Emotions in German Speech Using Gaussian Mixture Models Recognition of Emotions in German Speech Using Gaussian Mixture Models

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