Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients' speech signal with unilateral vocal fold paralysis.

Unilateral vocal fold paralysis (UVFP) is one of the most severe types of neurogenic laryngeal disorder in which the patients, due to their vocal cords malfunction, are confronted by some serious problems. As the effect of such pathologies would be significantly evident in the reduced quality and feature variation of dysphonic voices, this study is designed to scrutinize the piecewise variation of some specific types of these features, known as energy and entropy, all over the frequency range of pathological speech signals. In order to do so, the wavelet-packet coefficients, in five consecutive levels of decomposition, are used to extract the energy and entropy measures at different spectral sub-bands. As the decomposition procedure leads to a set of high-dimensional feature vectors, genetic algorithm is invoked to search for a group of optimal sub-band indexes for which the extracted features result in the highest recognition rate for pathological and normal subjects' classification. The results of our simulations, using support vector machine classifier, show that the highest recognition rate, for both optimized energy and entropy measures, is achieved at the fifth level of wavelet-packet decomposition. It is also found that entropy feature, with the highest recognition rate of 100% vs. 93.62% for energy, is more prominent in discriminating patients with UVFP from normal subjects. Therefore, entropy feature, in comparison with energy, demonstrates a more efficient description of such pathological voices and provides us a valuable tool for clinical diagnosis of unilateral laryngeal paralysis.