Recognition of Emotions in German Speech Using Gaussian Mixture Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985882%3A_____%2F09%3A00356050" target="_blank" >RIV/67985882:_____/09:00356050 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Recognition of Emotions in German Speech Using Gaussian Mixture Models
Original language 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.
Czech name
—
Czech description
—

Project
<a href="/en/project/OC08010" target="_blank" >OC08010: Emotional speech style analysis, modeling and synthesis</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
MULTIMODAL SIGNAL: COGNITIVE AND ALGORITHMIC ISSUES
ISBN
978-3-642-00524-4
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
—
Publisher name
SPRINGER-VERLAG
Place of publication
Berlin
Event location
Vietri sul Mare
Event date
Apr 21, 2008
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000265464200026

Similar results(10)