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

TY - JOUR

T1 - Different in different ways

T2 - A network-analysis approach to voice and prosody in Autism Spectrum Disorder

AU - Weed, Ethan

AU - Fusaroli, Riccardo

AU - Simmons, Elizabeth Schoen

AU - Eigsti, Inge-Marie

PY - 2024

Y1 - 2024

N2 - The current study investigated whether the difficulty in finding group differ- ences in prosody between speakers with autism spectrum disorder (ASD) and neurotypical (NT) speakers might be explained by identifying different acous- tic profiles of speakers which, while still perceived as atypical, might be characterized by different acoustic qualities. We modelled the speech from a selection of speakers (N = 26), with and without ASD, as a network of nodes defined by acoustic features. We used a community-detection algorithm to identify clusters of speakers who were acoustically similar and compared these clusters with atypicality ratings by naïve and expert human raters. Results identified 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. The human raters were highly reliable at distinguishing speakers with and without ASD, regardless of which cluster the speaker was in. These results suggest that community-detection methods using a net- work approach may complement commonly-employed human ratings to improve our understanding of the intonation profiles in ASD.

AB - The current study investigated whether the difficulty in finding group differ- ences in prosody between speakers with autism spectrum disorder (ASD) and neurotypical (NT) speakers might be explained by identifying different acous- tic profiles of speakers which, while still perceived as atypical, might be characterized by different acoustic qualities. We modelled the speech from a selection of speakers (N = 26), with and without ASD, as a network of nodes defined by acoustic features. We used a community-detection algorithm to identify clusters of speakers who were acoustically similar and compared these clusters with atypicality ratings by naïve and expert human raters. Results identified 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. The human raters were highly reliable at distinguishing speakers with and without ASD, regardless of which cluster the speaker was in. These results suggest that community-detection methods using a net- work approach may complement commonly-employed human ratings to improve our understanding of the intonation profiles in ASD.

KW - autistm

KW - prosody

KW - network analysis

U2 - 10.1080/15475441.2023.2196528

DO - 10.1080/15475441.2023.2196528

M3 - Journal article

C2 - 38486613

SN - 1547-3341

VL - 20

SP - 40

EP - 57

JO - Language Learning and Development

JF - Language Learning and Development

IS - 1

ER -