Continuous Tracking of User Emotion in Mandarin Emotional Speech

Abstract

Emotions play a significant role in decision-making, healthy, perception, human interaction and human intelligence. Automatic recognition of emotion in speech is very desirable because it adds to the human- computer interaction and becomes an important research area in the last years. However, to the best of our knowledge, no works have focused on automatic emotion tracking of continuous Mandarin emotional speech. In this paper, we present an emotion tracking system, by dividing the utterance into several independent segments, each of which contains a single emotional category. Experimental results reveal that the proposed system produces satisfactory results. On our testing database composed of 279 utterances which are obtained by concatenating short sentences, the average accuracy achieves 83% by using weighted D-KNN classifier and LPCC and MFCC features.