Rethinking Prosody Production in Autism: Nuanced Insights from Individual Differences and Network Analysis Approaches

American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org

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19

National Institute on Deafness and Other Communication Disorders (NIDCD) (DHHS/NIH)

T32DC013017

Autism Spectrum Disorders; Suprasegmentals; Children; Adolescents; Speech Communication; Language Skills; Articulation (Speech); Acoustics; Individual Differences

Clinical Evaluation of Language Fundamentals; Kaufman Brief Intelligence Test; Autism Diagnostic Observation Schedule; Woodcock Reading Mastery Test; Woodcock Johnson Tests of Achievement; Test of Word Reading Efficiency

10.1044/2025_JSLHR-24-00690

1092-4388
1558-9102

Purpose: Prosodic differences between autistic and non-autistic individuals are recognized, but there is a lack of consensus on the specific prosodic features that characterize the "autistic voice" due to widespread heterogeneity and mixed findings. This study seeks to build further understanding of the nuances of prosody in autism through individual differences and network analyses. Method: Acoustic analyses were conducted from 66 school-age autistic and non-autistic children and adolescents' narrative generation. Between-groups analyses of pitch- and timing-related prosodic features were conducted, followed by within-group analyses investigating associations between prosodic features and individual differences in overall language skills. Thereafter, established network analysis methods were adopted to detect the communities of participants based on similar prosodic features. Results: Initial between-groups analyses revealed greater pitch range and variation among autistic compared to non-autistic participants, as well as slower speech and articulation rates, although subsequent analyses revealed that speech and articulation rates were associated with overall language skills. Similar to Weed et al. (2024), the community detection algorithm identified three communities of participants clustered by prosodic features (pitch variation, speech and articulation rates, jitter), with various proportions of autistic participants in each community that did not effectively distinguish between autistic and non-autistic participants. Conclusions: Although between-groups differences consistent with similar previous literature have been indicated, community detection analyses further support the notion that prosody in autism may be "different in different ways." This work highlights the importance of moving beyond group-difference approaches in uncovering nuances to individual differences in prosody via within-group and data-driven analysis approaches.

As Provided

2026

EJ1497141