Emotional state recognition from speech via soft-competition on different acoustic representations

This paper presents our investigations on automatic emotional state recognition from speech signals using ensemble based methods based on different acoustic representations/feature measures. In our work, we employ various types of acoustic feature measures where none of the feature measures is optimal for emotional state classification. It is observed that different feature measures may be complementary and used simultaneously to yield a robust classification performance. Therefore, we employ a probabilistic method of combining classifiers based on different feature measures. The combination method that uses different feature measures simultaneously yields high recognition rates on various emotional speech corpora for both full feature set and language-independent feature subset. The ensemble method also outperforms a composite-feature representation and two other methods reported in literature. In addition, the classification accuracies achieved by our combination method are competitive with those mentioned in literature for different emotional speech corpora.