Evidence map›Paper›PMID 41532982›Full record

ArticleJournal of speech, language, and hearing research : JSLHR2026

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

Talia Liu, Kelsey E Davison, Ayelet M Kershenbaum, Ethan Weed, John D E Gabrieli, Helen Tager-Flusberg, Jennifer Zuk

Abstract read
In one paragraph

Article in Journal of speech, language, and hearing research : JSLHR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Talia LiuDepartment of Speech, Language & Hearing Sciences, Boston University, MA.ORCID 0000-0003-3534-9758
Kelsey E DavisonDepartment of Speech, Language & Hearing Sciences, Boston University, MA.ORCID 0000-0002-8845-1548
Ayelet M KershenbaumProgram in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA.ORCID 0000-0002-5177-8387
Ethan WeedDepartment of Linguistics, Cognitive Science and Semiotics, Aarhus University, Denmark.ORCID 0000-0002-3921-9101
John D E GabrieliMcGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge.ORCID 0000-0003-1158-5692
Helen Tager-FlusbergDepartment of Psychological & Brain Sciences, Boston University, MA.ORCID 0000-0002-8768-5414
Jennifer ZukDepartment of Speech, Language & Hearing Sciences, Boston University, MA.ORCID 0000-0001-9113-3973

Funding

Advanced Research Training in Communication Sciences and DisordersT32DC013017 · NIDCD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Cara E. Stepp · 2016 to 2026
$4.9M
Brain Bases of Language Deficits in SLI and ASDR01DC011339 · NIDCD · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GABRIELI, JOHN, TAGER-FLUSBERG, HELEN · 2011 to 2015
$3.1M
NIDCD NIH HHS R01 DC011339NIDCD NIH HHS T32 DC013017
6 · The paper itself

Abstract

purposeProsodic 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.

methodAcoustic 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.

resultsInitial 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.

conclusionsAlthough 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.

Indexed as

Autistic DisorderIndividualitySpeechSpeech AcousticsAdolescentChildFemaleHumansMaleSpeech Production Measurement

Identifiers

PMID41532982
PMCPMC12908236

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.