Discrimination Between Pathological and Normal Voices Using GMM-SVM Approach

Abstract

Acoustic features of vocal tract function are used widely in the study of pathological voices detection. Classification of normal and pathological voices by acoustic parameters is a useful way to diagnose voice diseases. In this aspect, mel-frequency cepstral coefficients are proved to be effective with traditional classifiers such as Gaussian Mixture Model (GMM). However, the accuracy of the classification method can be further improved. In this article, a Gaussian mixture model supervector kernel-support vector machine (GMM-SVM) classifier is compared with GMM classifier for the detection of voice pathology. We found that a sustain vowel phonation can be classified as normal or pathological with an accuracy of 96.1%. Voice recordings are selected from the Kay database to carry out the experiments. Experimental results show that equal error rates decrease from 8.0% for GMM to 4.6% for GMM-SVM.