10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Improving Automatic Emotion Recognition from Speech Signals

Elif Bozkurt (1), Engin Erzin (1), Çiǧdem Eroǧlu Erdem (2), A. Tanju Erdem (3)

(1) Koç University, Turkey
(2) Bahçeşehir University, Turkey
(3) Özyeğin University, Turkey

We present a speech signal driven emotion recognition system. Our system is trained and tested with the INTERSPEECH 2009 Emotion Challenge corpus, which includes spontaneous and emotionally rich recordings. The challenge includes classifier and feature sub-challenges with five-class and two-class classification problems. We investigate prosody related, spectral and HMM-based features for the evaluation of emotion recognition with Gaussian mixture model (GMM) based classifiers. Spectral features consist of mel-scale cepstral coefficients (MFCC), line spectral frequency (LSF) features and their derivatives, whereas prosody-related features consist of mean normalized values of pitch, first derivative of pitch and intensity. Unsupervised training of HMM structures are employed to define prosody related temporal features for the emotion recognition problem. We also investigate data fusion of different features and decision fusion of different classifiers, which are not well studied for emotion recognition framework. Experimental results of automatic emotion recognition with the INTERSPEECH 2009 Emotion Challenge corpus are presented.

Full Paper

Bibliographic reference.  Bozkurt, Elif / Erzin, Engin / Erdem, Çiǧdem Eroǧlu / Erdem, A. Tanju (2009): "Improving automatic emotion recognition from speech signals", In INTERSPEECH-2009, 324-327.