Classification of Normal and Pathological Voice using GA and SVM

Abstract

analysis of pathological voice is a challenging and an important area of research in speech processing. Acoustic characteristics of voice are used mainly to discriminate normal voices from pathological voices. This study explores methods to find the ability of acoustic parameters in discrimination of normal voices from pathological voices. An attempt is made to analyze and to classify pathological voice from normal voice in children. The classification of pathological voice from normal voice is implemented using support vector machine (SVM). The normal and pathological voices of children are used to train and test the classifier. A dataset is constructed by recording speech utterances of a set of Tamil phrases. The speech signal is then analyzed in order to extract the acoustic parameters such as the Signal Energy, pitch, formant frequencies, Mean Square Residual signal, Reflection coefficients, Jitter and Shimmer. In this study various acoustic features are combined to form a feature set, so as to detect voice disorders in children based on which further treatments can be prescribed by a pathologist. A Genetic Algorithm (GA) based feature selection is utilized to select best set of features which improves the classification accuracy.