A novel feature selection method for speech emotion recognition

Abstract

Speech emotion recognition involves analyzing vocal changes caused by emotions with acoustic analysis and determining the features to be used for emotion recognition. The number of features obtained by acoustic analysis reaches very high values depending on the number of acoustic parameters used and statistical variations of these parameters. Not all of these features are effective for emotion recognition; in addition, different emotions may effect different vocal features. For this reason, feature selection methods are used to increase the emotional recognition success and reduce workload with fewer features. There is no certainty that existing feature selection methods increase/decrease emotion recognition success; some of these methods increase the total workload. In this study, a new statistical feature selection method is proposed based on the changes in emotions on acoustic features. The success of the proposed method is compared with other methods mostly used in literature. The comparison was made based on number of feature and emotion recognition success. According to the results obtained, the proposed method provides a significant reduction in the number of features, as well as increasing the classification success.