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January 16, 2026Journal of Speech Language and Hearing Research0 citations

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

TLTalia Liu KDKelsey E. DavisonBoston University AKAyelet M. KershenbaumHarvard University

Key Points

The study aims to understand prosodic differences between autistic and non-autistic individuals, focusing on individual variances.
Conducted acoustic analyses on narrative generation from 66 children and adolescents.
Performed between-groups analyses of pitch and timing-related prosodic features.
Investigated associations between prosodic features and language skills within groups.
Applied network analysis methods to identify clusters of participants based on prosodic features.
Identified greater pitch range and variation among autistic compared to non-autistic participants.
Found slower speech and articulation rates in autistic individuals, associated with language skills.
Detected three communities of participants clustered by prosodic features, showing mixed proportions of autistic individuals in each community.

Abstract

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. Supplemental Material: https://doi.org/10.23641/asha.31011862

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