Pathologic discrimination through acoustic analysis of voice

Abstract

The acoustic analysis of voice from dysphonic patients has been studied, mainly for classification of voice quality in terms of roughness, breathiness, and hoarseness. By a data reduction of speech signal, researchers have produced several acoustic measures in order to relate them with a perceptual diagnosis. In this work, a proposal was made to modify acoustic parameters in order to support a higher sample quantity, instead of using only a data window by digitalized speech. Also, robust statistics is used to obtain realiable measures that estimate the abnormal condition of a voice signal as well as to discriminate several pathologics of the larynx and vocal cords, without any perceptual consideration. Using only seven measures from adaptative inverse filtering (Kalman and Wiener filters) and a neural network for pattern classification on 228 voice signals of speakers (20 groups of distinct types of pathologies and one of normal speakers) evaluated through videolaringoscopy, results of the pathologic discrimination showed 22% of uncertainty. Redrawing the patient sample in seven distinct groups (five with defined pathologies, one with all others pathologies, and one control), then the error in the pathologic discrimination is 18%. [Work supported by Capes.]