A novel hierarchical speech emotion recognition method based on improved DDAGSVM

Abstract

In order to improve the recognition accuracy of speech emotion recognition, in this paper, a novel hierarchical method based on improved Decision Directed Acyclic Graph SVM (improved DDAGSVM) is proposed for speech emotion recognition. The improved DDAGSVM is constructed according to the confusion degrees of emotion pairs. In addition, a geodesic distance-based testing algorithm is proposed for the improved DDAGSVM to give the test samples differently distinguished many decision chances. Informative features and SVM optimized parameters used in each node of the improved DDAGSVM are gotten by Genetic Algorithm (GA) synchronously. On the Chinese Speech Emotion Database (CSED) and the Audio-Video Emotion Database (AVED) recorded by our workgroup, the recognition experiment results reveal that, compared with multi-SVM, binary decision tree and traditional DDAGSVM, the improved DDAGSVM has the higher recognition accuracy with few selected informative features and moderate time for 7 emotions.