Feature selection based on binary particle swarm optimization and neural networks for pathological voice detection

Laryngeal pathologies directly affect the quality of the voice. In the last years, digital signal processing techniques have been applied for detection of vocal fold pathologies through speech signal analysis. In this work, a binary Particle Swarm Optimization (PSO) algorithm using Multilayer Perceptron (MLP) neural network is employed for the selection of the most significative features in a pathological voice detection system. The discrimination between healthy and pathological speech signals are obtained by 52 Haralick texture features, extracted from two-dimensional wavelet coefficients of the speech signals recurrence plots. The fitness function adopted is based in the maxima accuracy rate. Experimental results show that the use of PSO increased the accuracy rates with a minimum number of features.