Analysis of High-level Features for Vocal Emotion Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F11%3APU95202" target="_blank" >RIV/00216305:26220/11:PU95202 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of High-level Features for Vocal Emotion Recognition

Popis výsledku v původním jazyce

The paper deals with the vocal emotion recognition which is a very important task for several applications in the field of human-machine interaction. There is a plenty of algorithms proposed up to date for this purpose that exploit different types of features and classifiers. Our previous work showed that high-level features perform very well in terms of emotion classification from speech. However, little attention has been paid so far to the statistical analysis of these features. For this reason the presented paper mainly focuses on the emotion recognition by using only high-level features. Two different emotional speech corpora were exploited in our experiments, namely the Berlin Database of Emotional Speech and the COST2102 Italian Database of Emotional Speech. Results showed that the best high-level features in terms of high discriminative power strongly differ among the databases considered on the first hand and among the emotions within each database on the second hand.

Název v anglickém jazyce

Analysis of High-level Features for Vocal Emotion Recognition

Popis výsledku anglicky

The paper deals with the vocal emotion recognition which is a very important task for several applications in the field of human-machine interaction. There is a plenty of algorithms proposed up to date for this purpose that exploit different types of features and classifiers. Our previous work showed that high-level features perform very well in terms of emotion classification from speech. However, little attention has been paid so far to the statistical analysis of these features. For this reason the presented paper mainly focuses on the emotion recognition by using only high-level features. Two different emotional speech corpora were exploited in our experiments, namely the Berlin Database of Emotional Speech and the COST2102 Italian Database of Emotional Speech. Results showed that the best high-level features in terms of high discriminative power strongly differ among the databases considered on the first hand and among the emotions within each database on the second hand.

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/FR-TI1%2F481" target="_blank" >FR-TI1/481: Technologie pokročilých analýz mluvené řeči pro kontaktní centra a bezpečnostní složky.</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

34th International Conference on Telecommunications and Signal Processing

ISBN

978-1-4577-1409-2

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

361-366

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Budapest

Datum konání akce

18. 8. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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