Emotion Recognition from Persian Speech with Neural Network

Abstract

In this paper, we report an effort towards automatic recognition of emotional states from continuous Persian speech. Due to the unavailability of appropriate database in the Persian language for emotion recognition, at first, we built a database of emotional speech in Persian. This database consists of 2400 wave clips modulated with anger, disgust, fear, sadness, happiness and normal emotions. Then we extract prosodic features, including features related to the pitch, intensity and global characteristics of the speech signal. Finally, we applied neural networks for automatic recognition of emotion. The resulting average accuracy was about 78%.