Performance of Different Acoustic Measures to Discriminate Individuals With and Without Voice Disorders

Abstract

The goal of this study is to compare and combine different acoustic features in discriminating subjects with and without voice disorders. A database of 484 adult patients participated in the research. All subjects recorded a sustained vowel /Ɛ/ and underwent a laryngoscopic examination of the larynx. From the results of the laryngeal examination performed by a physician and the auditory-perceptual judgment performed by a Speech-Language Pathologist, the subjects were allocated to the group with (n=52) and without (n=432) voice disorder. Four types of acoustic features were used: traditional measures, cepstral measures, nonlinear measures, and recurrence quantification measures. Recordings comprised the emission of the vowel /ε/. Quadratic discriminant analysis was used as classifier. Individual features in the context of traditional, cepstral, and recurrence quantification measures achieved an acceptable performance of ≥70%. Combination of measures improved the classifier performance. The best classification result (86.43% accuracy) was obtained by combining traditional linear and recurrence quantification measures. Results shown that Traditional, Cepstral, and recurrence quantification measures are promising features that capture meaningful information about voice production, which provides good classification performances. The findings of this study can be used to develop a computational tool for voice disorders diagnosis and monitoring.