A Speaker Independent Approach to the Classification of Emotional Vocal Expressions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F08%3APU75604" target="_blank" >RIV/00216305:26220/08:PU75604 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

čeština

Název v původním jazyce

A Speaker Independent Approach to the Classification of Emotional Vocal Expressions

Popis výsledku v původním jazyce

The paper proposes a speaker independent procedure for classifying vocal expressions of emotion. The procedure is based on the splitting up of the emotion recognition process into two steps. In the first step, a combination of selected acoustic featuresis used to classify six emotions through a Bayesian Gaussian Mixture Model classifier (GMM). The two emotions that obtain the highest likelihood scores are selected for further processing in order to discriminate between them. For this purpose, a uniqueset of high-level acoustic features was identified using the Sequential Floating Forward Selection (SFFS) algorithm, and a GMM was used to separate between each couple of emotion. The mean classification rate is 81% with an improvement of 5% with respectto the most recent results obtained on the same database (75%).

Název v anglickém jazyce

A Speaker Independent Approach to the Classification of Emotional Vocal Expressions

Popis výsledku anglicky

The paper proposes a speaker independent procedure for classifying vocal expressions of emotion. The procedure is based on the splitting up of the emotion recognition process into two steps. In the first step, a combination of selected acoustic featuresis used to classify six emotions through a Bayesian Gaussian Mixture Model classifier (GMM). The two emotions that obtain the highest likelihood scores are selected for further processing in order to discriminate between them. For this purpose, a uniqueset of high-level acoustic features was identified using the Sequential Floating Forward Selection (SFFS) algorithm, and a GMM was used to separate between each couple of emotion. The mean classification rate is 81% with an improvement of 5% with respectto the most recent results obtained on the same database (75%).

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/GA102%2F07%2F1303" target="_blank" >GA102/07/1303: Nelineární metody zvýrazňování řeči</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of Twentieth International Conference on Tools With Artificial Intelligence, ICTAI 2008

ISBN

978-0-7695-3440-4

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

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Název nakladatele

IEEE Computer Society

Místo vydání

Dayton, Ohio, USA

Místo konání akce

Dayton, Ohio

Datum konání akce

3. 11. 2008

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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