Survey on discriminative feature selection for speech emotion recognition

Increasing attention has been paid to the study of the emotional content of speech signals recently. Most studies focus on classification schemes and few of them concentrate on the selection of suitable features for speech emotion recognition. Current studies still encounter many bottlenecks in both speech emotion recognition and emotional speech synthesis because of the lack of the fine modeling of emotions. This paper takes two basic emotions including happiness and sadness as an example to explore the fine modeling of emotions, aiming at providing necessary phonetic clues for speech emotion recognition and emotional speech synthesis. 33 acoustic features are selected to form the original feature set based on the comprehensive feature analysis in the first step. The best performing feature subset is chosen according to the recognition experiments and it will verify the results of the comprehensive feature analysis. The experimental results show that the emotion recognition model with features set we final select achieves higher classification accuracy than other features set and the smaller data of features set can reduce model complexity. The importance of each acoustic feature is also analyzed in this paper.