HUMAN EMOTION RECOGNITION USING SPEECH FEATURES

Abstract

The focus of this thesis is on emotion recognition based on the speech signal. The state of the art is being reviewed. Based on models in psychology and the requirements of automatic systems, models for emotion recognition from speech are proposed and a most appropriate one for automatic detection is chosen. The aim is to investigate the algorithm of speech emotion recognition. Firstly, five most commonly used features are selected and extracted from speech signal. After this, statistical values such as mean, variance will be derived from the features. These data along with their related emotion target will be fed to neural network tool to train and test to make up the classifier. We use feed forward back propagation neural network for classification, both for speaker dependent and speaker independent system. The research work has been done using 120 different sentences spoken by two male speakers. The performance of 90% recognition rate for speaker dependent and 75% recognition rate for speaker independent system.