Automatic Voice Classification Of Autistic Subjects

TL;DR

This paper proposes an automatic speech classification algorithm to identify prosodic features distinguishing autistic individuals, aiming to support clinical diagnosis amidst the heterogeneity of autism spectrum disorders.

Contribution

The study introduces a novel speech-based classification method specifically designed for autism detection, utilizing prosodic features to improve diagnostic support.

Findings

Effective classification of autistic vs. non-autistic speech

Prosodic features significantly distinguish autism

Supports clinical diagnosis with automated tools

Abstract

Autism Spectrum Disorders (ASD) describe a heterogeneous set of conditions classified as neurodevelopmental disorders. Although the mechanisms underlying ASD are not yet fully understood, more recent literature focused on multiple genetics and/or environmental risk factors. Heterogeneity of symptoms, especially in milder forms of this condition, could be a challenge for the clinician. In this work, an automatic speech classification algorithm is proposed to characterize the prosodic elements that best distinguish autism, to support the traditional diagnosis. The performance of the proposed algorithm is evaluted by testing the classification algorithms on a dataset composed of recorded speeches, collected among both autustic and non autistic subjects.