Emotion Recognition in Speech Using Neural Network

Abstract

Emotion recognition in speech studies has grown after the growing of speech recognition technologies. The aim of such studies is to make language interfaces in human-computer interaction applications more wide in use and to make it efficient. And it may help the studiers of the human sound areas. This study deals with four spoken sentences of sixteen short utterance expressing five emotions: a happiness, anger, sadness, fear, and normal (unemotional) state. The feature extraction techniques are used to capture the most important information of the signal, this process is applied in the time domain. The extracted feature vector contain 12 LPC (linear predictive coding) parameters, the pitch, the power, and the three first formant frequencies. While in the second part of the system (the emotion recognizer) the artificial neural network technology is used, the designing network is with feedforward backpropagation. This design used ten separated nets. each one make a decision whether the spoken sentence is belong to one of only tow emotions. So the computational time is increased with this system, while such a system has better performance with respect to other systems. The designed system could recognize all emotions with ratio 75% except the fear one which recognized in ratio of 50%.