Cross-linguistic patterns of speech prosodic differences in autism: A machine learning study

Joseph C.Y. Lau, Shivani Patel, Xin Kang, Kritika Nayar, Gary E. Martin, Jason Choy, Patrick C.M. Wong, Molly Losh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Differences in speech prosody are a widely observed feature of Autism Spectrum Disorder (ASD). However, it is unclear how prosodic differences in ASD manifest across different languages that demonstrate cross-linguistic variability in prosody. Using a supervised machine-learning analytic approach, we examined acoustic features relevant to rhythmic and intonational aspects of prosody derived from narrative samples elicited in English and Cantonese, two typologically and prosodically distinct languages. Our models revealed successful classification of ASD diagnosis using rhythm-relative features within and across both languages. Classification with intonation-relevant features was significant for English but not Cantonese. Results highlight differences in rhythm as a key prosodic feature impacted in ASD, and also demonstrate important variability in other prosodic properties that appear to be modulated by language-specific differences, such as intonation.

Original languageEnglish (US)
Article numbere0269637
JournalPloS one
Volume17
Issue number6 June
DOIs
StatePublished - Jun 2022

ASJC Scopus subject areas

  • General

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