Voice pathology assessment using wavelet based on texture analysis of recurrence plots

This work proposes an efficient texture classification strategy performed in the wavelet domain in order to characterize healthy and pathological speech signals from recurrence plots (RP). The two-dimensional wavelet transform is applied to the recurrence plots at one resolution level. Thirteen Haralick texture features are obtained from each approximation and detail subband coefficients. In classification, multilayer perceptron (MLP) neural networks with cross validation are employed. Classification accuracy is improved and the number of features is reduced by particle swarm optimization (PSO). Results suggest that this method may be useful for pathological voice discrimination.