Machine learning based voice analysis in spasmodic dysphonia: an investigation of most relevant features from specific vocal tasks

Abstract

Adductor-type spasmodic dysphonia (ASD) is a task-specific speech disorder characterized by a strangled and strained voice. We have previously demonstrated that advanced voice analysis, performed with support vector machine, can objectively quantify voice impairment in dysphonic patients, also evidencing results of voice improvements due to symptomatic treatment with botulinum neurotoxin type-A injections into the vocal cords. Here, we expanded the analysis by means of three different machine learning algorithms (Support Vector Machine, Naïve Bayes and Multilayer Percept), on a cohort of 60 ASD patients, some of them also treated with botulinum neurotoxin type A therapy, and 60 age and gender-matched healthy subjects. Our analysis was based on sounds produced by speakers during the emission of /a/ and /e/ sustained vowels and a standardized sentence. As a conclusion, we report the main features with discriminatory capabilities to distinguish untreated vs. treated ASD patients vs. healthy subjects, and a comparison of the three classifiers with respect to their discriminating accuracy