A Robust Method for Speech Emotion Recognition Based on Infinite Student’s -Mixture Model

Speech emotion classification method, proposed in this paper, is based on Student’s -mixture model with infinite component number (iSMM) and can directly conduct effective recognition for various kinds of speech emotion samples. Compared with the traditional GMM (Gaussian mixture model), speech emotion model based on Student’s -mixture can effectively handle speech sample outliers that exist in the emotion feature space. Moreover, -mixture model could keep robust to atypical emotion test data. In allusion to the high data complexity caused by high-dimensional space and the problem of insufficient training samples, a global latent space is joined to emotion model. Such an approach makes the number of components divided infinite and forms an iSMM emotion model, which can automatically determine the best number of components with lower complexity to complete various kinds of emotion characteristics data classification. Conducted over one spontaneous (FAU Aibo Emotion Corpus) and two acting (DES and EMO-DB) universal speech emotion databases which have high-dimensional feature samples and diversiform data distributions, the iSMM maintains better recognition performance than the comparisons. Thus, the effectiveness and generalization to the high-dimensional data and the outliers are verified. Hereby, the iSMM emotion model is verified as a robust method with the validity and generalization to outliers and high-dimensional emotion characters.