Recognition of Emotions in German Speech Using Gaussian Mixture Models
1.
SYSNO ASEP
0356050
Document Type
C - Proceedings Paper (int. conf.)
R&D Document Type
Conference Paper
Title
Recognition of Emotions in German Speech Using Gaussian Mixture Models
Author(s)
Vondra, Martin (URE-Y) Vích, Robert (URE-Y)
Number of authors
2
Source Title
MULTIMODAL SIGNAL: COGNITIVE AND ALGORITHMIC ISSUES, 5398. - Berlin : SPRINGER-VERLAG, 2009 / Esposito A. ; Hussain A. ; Marinaro M. ; Martone R.
- ISSN 0302-9743
- ISBN 978-3-642-00524-4
Pages
s. 256-263
Number of pages
8 s.
Action
euCognition International Training School on Multimodal Signals - Cognitive and Algorithmic Issues (European COST A2102)
Event date
21.04.2008-26.04.2008
VEvent location
Vietri sul Mare
Country
IT - Italy
Event type
WRD
Language
eng - English
Country
DE - Germany
Keywords
emotion recognition ; speech emotions
Subject RIV
JA - Electronics ; Optoelectronics, Electrical Engineering
R&D Projects
OC08010 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
CEZ
AV0Z20670512 - URE-Y (2005-2011)
UT WOS
000265464200026
Annotation
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.
