Qi-rong Mao¹ and Yong-zhao Zhan¹

¹ Department of Computer Science and Communication Engineering of Jiangsu University,
zhenjiang, Jiangsu Province, 212013, China
{mao_qr,yzzhan}@ujs.edu.cn
 

Abstract. Since error classification cumulation in hierarchical method impacts the classification ability of it, 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 Berlin Emotional Speech Database (BESD), 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.