Adaptive estimation of residue signal for voice pathology diagnosis

Abstract

The use of noninvasive techniques to evaluate the larynx and vocal tract helps the speech specialists to perform accurate diagnose of diseases. In this study, a method to distinguish among 21 different pathologies using speech signals was developed. Through inverse filtering (Kalman and Wiener filters) of the voice signal, the residue was estimated and seven acoustic features were extracted from it to evaluate the laryngeal diseases. As time-invariant inverse filtering was used, the nonstationary nature of dysphonic voices was also considered. Together with the estimation of the acoustic features using a robust statistical method, this technique also allowed us to discriminate among pathologies with very close perceptual characteristics. The results from a Mann-Whitney test indicated that the best measurement for pathological discrimination was JITTER with 54.79% ability to cluster the voice types and the worst one was spectral flatness of residue (SFR) with 36.41%.