Speech emotion recognition based on dynamic models

This paper introduced the semi-continuous Hidden Markov Model (HMM) and proposed a novel Dynamic Bayesian Network (DBN) model for dynamic speech emotion recognition. The former reduces the training complexity caused by mixture Gaussians by sharing the Condition Probability Densities (CPDs) of Gaussians among the states, and the latter adds a sub-state layer between state and observation layer based on traditional DBN framework and describes the dynamic process of speech emotion in detail. Experiments results show that average emotion recognition rate of semi-continuous HMM is 4% and 10% higher than those of classical HMM and Mixture Gaussian HMM respectively, and average emotion recognition rate of the three-layer DBN model is 11% and 8% higher than those of traditional DBN model and semi-continuous HMM.