Feature selection filtering methods for emotion recognition in Chinese speech signal

Nowadays, recognizing human emotion in speech signal has attracted much attention and plays an important role in affect computing, artificial intelligence and signal processing areas. In this paper, seven feature selection filtering methods, including CFS, Chisquare, consistency, gain ratio, Infogain, relief and symmetrical uncertainty, are proposed to perform feature selection from 47 original features extracted from natural emotional speech corpus in an effort to yield less features capable of discriminating between emotion categories. The results of feature selection were evaluated through a simple k-nearest-neighbors classifier. Experiment results indicate that the most important two features among all extracted features for emotion recognition are ratio of voiced to unvoiced frames and intensity max, achieving acceptable emotion recognition rate of 67%. Additionally, the features selected by Relief can obtain the highest accuracy of 72% with 9 features, outperforming all other feature selection methods and exceeding the mean accuracy of 71.5% with all features.