Comparison of adaptation methods for GMM-SVM based speech emotion recognition

The required length of the utterance is one of the key factors affecting the performance of automatic emotion recognition. To gain the accuracy rate of emotion distinction, adaptation algorithms that can be manipulated on short utterances are highly essential. Regarding this, this paper compares two classical model adaptation methods, maximum a posteriori (MAP) and maximum likelihood linear regression (MLLR), in GMM-SVM based emotion recognition, and tries to find which method can perform better on different length of the enrollment of the utterances. Experiment results show that MLLR adaptation performs better for very short enrollment utterances (with the length shorter than 2s) while MAP adaptation is more effective for longer utterances.