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 language | English (US) |
|---|---|
| Article number | e0269637 |
| Journal | PloS one |
| Volume | 17 |
| Issue number | 6 June |
| DOIs | |
| State | Published - Jun 2022 |
Funding
This research was supported by grants from the National Institutes of Health (USA: R01DC010191, R03DC018644, PI: ML), Health and Medical Research Fund (Hong Kong: 02130846, PI: PW), Global Parent Child Resource Centre Limited (to PW), and Dr Stanley Ho Medical Development Foundation (to PW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to thank families in the U.S. and Hong Kong who participated in this research study.
ASJC Scopus subject areas
- General