Identification of vocal and voice disorders

Abstract

The discrimination of normal and pathological voices using noninvasive acoustic analysis helps to perform accurate identification of voice disorders and diagnoses of vocal and voice disease. Acoustic analysis is a non- invasive technique based on digital processing of the speech signal. In the recent years, acoustic analysis of normal and pathological voices have become increasingly interesting to researchers in laryngology and speech pathologies. This paper presents classification of pathological voices using neural network trained by Back propagation algorithm with slope parameter and BP with binary sigmoidal and Gaussian activation function. Simulation results indicate that the proposed algorithm provide better classification rate than conventional back propagation algorithm.