Voice disorder classification using MLP and wavelet packet transform

Speech consists of vocal sounds resulting from the synchronism of all parts of the human phonatory apparatus. The oral communication between individuals is supposed to sound pleasant, and this characteristic is strongly related to the subjective parameters studied by phonoaudiology, such as roughness, breathiness, and strain. Commonly, these characteristics are evaluated with a noninvasive vocal disorder test. This paper proposes the development of an intelligent computational tool to classify and point out the preponderance of such parameters from audio samples. Moreover, a comparative study was made to evaluate the efficiency of several Wavelet families as feature extraction methods. The features extracted were used with artificial neural networks and an automatic routine was set up to find the best topology of the Multi-Layer Perceptron (MLP) architecture used in the classification of such speech parameters. The results are promising and reliable, with accuracy rates higher than 98.9%.