Speech Emotion Recognition in Acted and Spontaneous Context

Little attention has been paid so far in the context in which databases used for the study of emotion through vocal channel are recorded. Thus, we propose and evaluate an emotion classification system focusing on the differences between acted and spontaneous emotional speech through the use of two different databases: SAVEE and IEMOCAP. For the purpose of this work, we have examined wavelet packet energy and entropy features applied to Mel, Bark and ERB scale applied with Hidden Markov Model (HMM) as classification system. Experimental results show that the proposed method is a feasible technique for emotion classification for both acted and spontaneous context, pointing out the performance difference of the system between the two contexts. The experimental results shows that ERB scale features gives better performance in comparison with other studied features with recognition accuracy of 78.75% for acted context and 50.06% for spontaneous context.