Title :
GMM Supervector Based SVM with Spectral Features for Speech Emotion Recognition
Author :
Hao Hu ; Ming-Xing Xu ; Wei Wu
Author_Institution :
Center for Speech Technol., Tsinghua Univ., Beijing, China
Abstract :
Speech emotion recognition is a challenging yet important speech technology. In this paper, the GMM supervector based SVM is applied to this field with spectral features. A GMM is trained for each emotional utterance, and the corresponding GMM supervector is used as the input feature for SVM. Experimental results on an emotional speech database demonstrate that the GMM supervector based SVM outperforms standard GMM on speech emotion recognition.
Keywords :
Gaussian processes; emotion recognition; feature extraction; speech processing; speech recognition; support vector machines; GMM supervector; SVM; emotional speech database; spectral features; speech emotion recognition; Cepstral analysis; Emotion recognition; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Spatial databases; Speaker recognition; Speech; Support vector machine classification; Support vector machines; GMM supervector; SVM; Speech emotion recognition; spectral features;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0727-3
DOI :
10.1109/ICASSP.2007.366937
