Different in different ways: A network-analysis approach to voice and prosody in Autism Spectrum Disorder

Speakers with autism spectrum disorder (ASD) are commonly reported to have atypical voice and prosody that impact impression formation. While human raters are highly accurate in distinguishing between ASD and neurotypical (NT) speakers, there is little consensus on which specific acoustic features differentiate these groups, suggesting the presence of multiple prosodic profiles. To investigate this possibility, we modelled the speech from a selection of speakers (N = 30), with and without ASD, as a network of nodes defined by acoustic features, and used a community-detection algorithm to identify clusters of speakers who were acoustically similar. Analyses suggested three clusters: one primarily composed of speakers with ASD, one of mostly NT speakers, and one comprised of an even mixture of ASD and NT speakers. Human raters are highly reliable at distinguishing speakers with and without ASD based on perceptual voice and prosodic cues. Our results suggest that community-detection methods using a network approach may complement commonly employed human ratings to improve our understanding of the intonation profiles in ASD.