Automatic Recognition of Anger in Spontaneous Speech
2008 (English)In: INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2008, 2755-2758 p.Conference paper (Refereed)
Automatic detection of real life negative emotions in speech has been evaluated using Linear Discriminant Analysis, LDA, with "classic" emotion features and a classifier based on Gaussian Mixture Models, GMMs. The latter uses Mel-Frequency Cepstral Coefficients, MFCCs, from a filter bank covering the 300-3400 Hz region to capture spectral shape and formants, and another in the 20-600 Hz region to capture prosody. Both classifiers have been tested on an extensive corpus from Swedish voice controlled telephone services. The results indicate that it is possible to detect anger with reasonable accuracy (average recall 83%) in natural speech and that the GMM method performed better than the LDA one.
Place, publisher, year, edition, pages
BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2008. 2755-2758 p.
spontaneous speech, natural emotions, anger
Computer and Information Science Communication Studies
IdentifiersURN: urn:nbn:se:kth:diva-29858ISI: 000277026101268ScopusID: 2-s2.0-84867218213ISBN: 978-1-61567-378-0OAI: oai:DiVA.org:kth-29858DiVA: diva2:399555
9th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2008), Brisbane, AUSTRALIA, SEP 22-26, 2008
QC 201102222011-02-222011-02-172011-02-22Bibliographically approved