Emotional tone-based audio continuous emotion recognition

Abstract

Understanding human emotions in natural communication is still a challenge problem to be solved in human-computer interaction. Emotional tone that people feel within a period of time can affect the way people communicate with others or environment. In this paper, a new emotional tone-based two-stage algorithm for continuous emotion recognition from audio signals is presented. Gaussian mixture models of hidden Markov models (GMM-HMMs) are employed to infer the dimensional emotional tone and affect labels. Two emotional tones, positive or negative, which represent the overall emotion state over an audio clip are first obtained. Then, based on that emotional tone, corresponding positive or negative GMM-HMM classifier is refined to finish the continuous emotion recognition. The experimental results show that our method outperforms the GMM-HMM and SVR in baseline for the Audio-Visual Emotion Challenge (AVEC 2014) database [1].