GMM Supervector Based SVM with Spectral Features for Speech Emotion Recognition
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.