Method for Vocal Fold Paralysis Detection Based on Perceptual and Acoustic Assessment

Abstract

This study is aimed to evaluate a method for distinguishing between healthy and pathological voices. The evaluation was carried out using several acoustic parameters including COVAREP (collaborative voice analysis repository for speech technologies), the auditory-perceptual RBH (roughness, breathiness, hoarseness) scale, and AVQI (acoustic voice quality index). Finally, a classifier is trained using machine learning algorithms from the WEKA (Waikato Environment for Knowledge Analysis) platform. The study group comprised 75 voice recordings of individuals affected by vocal fold paralysis. The control group consisted of 49 voice recordings of healthy individuals. The results indicate that the voice quality of the study group is significantly different than the voice quality of the control group. Acoustic parameters implemented in COVAREP and the RBH scale have proven to be reliable methods assessing voice quality. In addition, data classification achieved over 90% accuracy for every classifier.