A hybrid expert system for automatic detection of voice disorders

Abstract

Pathological voice analysis is a challenging task and an important area of research in voice disorder identification. Until now, the long-time acoustic (LTA) parameters are used primitively to classify the disordered voices into pathological and normal. Selection of such optimal LTA features is a disputing task. Previous researchers have used various data projection methods like principle component analysis (PCA), linear discriminant analysis (LDA) and sub-optimal searching techniques like sequential forward selection (SFS), sequential backward selection (SBS), and individual feature selection (IFS) methods for this purpose. But, these methods work efficiently for linearly separable datasets only. In order to overcome these issues, we propose a hybrid expert system in this paper, which includes the optimal selection of LTA parameters using genetic algorithm (GA), followed by non-linear classification algorithms to classify the two classes of voice samples. Nowadays, though many non-linear and high-order spectral parameters of voices have been used in this application, LTA features are scoring more importance because their clinical diagnosis is of more ease. Within this context, the GA-based feature vector quantisation combined with SVM classification is demonstrated to be more reliable, yielding 96.86% of classification accuracy for a feature vector of length 10.