Author
Bozkurt, E., Erzin, E., Eroğlu Erdem, Ç., Erdem, Tanju
Publication Date
2010
Publication Place
-
IEEE
Subject
Gaussian processes, Emotion recognition, Signal classification, Signal representation, Speech recognition
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1051-4651
Record ID
fd611d87-0f31-4d6e-aa01-7f1ef8e66f9d
Library Location
Computer Science
Date
2010
Notes
TUBİTAK ; Bahçeşehir University Research Fund
Sample Text
We propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features such as mel-scaled cepstral coefficients have already been successfully used for the parameterization of speech signals for emotion recognition. The LSF features also offer a spectral representation for speech, moreover they carry intrinsic information on the formant structure as well, which are related to the emotional state of the speaker. We use the Gaussian mixture model (GMM) classifier architecture, that captures the static color of the spectral features. Experimental studies performed over the Berlin Emotional Speech Database and the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF features bring a consistent improvement over the MFCC based emotion classification rates.
DOI
10.1109/ICPR.2010.903
