Title: A hybrid expert system for automatic detection of voice disorders

Authors: R. Sindhu; Siew-Chin Neoh; M. Hariharan

Addresses: School of Microelectronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600, Campus Pauh Putra, Perlis, Malaysia. ' School of Microelectronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, Perlis, 02600, Malaysia ' School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, Perlis, 02600, Malaysia

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.

Keywords: voice disorders; support vector machine; SVM; feature selection; LTA features; genetic algorithms; hybrid expert systems; automatic detection; disease detection; long-time acoustic; LTA parameters; nonlinear classification; clinical diagnosis; vocal fold disorders.

DOI: 10.1504/IJMEI.2014.063179

International Journal of Medical Engineering and Informatics, 2014 Vol.6 No.3, pp.218 - 237

Available online: 04 Jul 2014 *

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