An Approach To Feature Selection Algorithm Based On Ant Colony Optimization For Human Emotion Recognition Using Speech

Abstract

Speech is one of the most promising models by which people can express their emotions like anger,sadness, and happiness. These states can be determined using various techniques apart from facial expressions. Acoustic parameters of a speech signal like energy, pitch, Mel Frequency Cepstral Coefficient (MFCC) are important in finding out the state of a person. In this project, the speech signal is taken as the input and by means of MFCC feature extraction method, cepstral coefficients are extracted by using MFCC. The large amount of extracted features may contain noise and other unwanted features. Hence, an evolutionary algorithm called as Ant Colony Optimization (ACO) is used as an efficient feature selection method. By using ACO technique the unwanted features are removed and only best feature subset is obtained. It is found that the total number of features extracted get reduced considerably. The software used is MATLAB