• DOI:10.1371/journal.pone.0225377
• Corpus ID: 208768328

Quantification of speech and synchrony in the conversation of adults with autism spectrum disorder

@article{Ochi2019QuantificationOS,
  title={Quantification of speech and synchrony in the conversation of adults with autism spectrum disorder},
  author={Keiko Ochi and Nobutaka Ono and Keiho Owada and Masaki Kojima and Miho Kuroda and Shigeki Sagayama and Hidenori Yamasue},
  journal={PLoS ONE},
  year={2019},
  volume={14}
}

• K. Ochi, Nobutaka Ono, +4 authors H. Yamasue
• Published 5 December 2019
• Psychology
• PLoS ONE

Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impairments in social reciprocity and communication together with restricted interest and stereotyped behaviors. The Autism Diagnostic Observation Schedule (ADOS) is considered a ‘gold standard’ instrument for diagnosis of ASD and mainly depends on subjective assessments made by trained clinicians. To develop a quantitative and objective surrogate marker for ASD symptoms, we investigated speech features including F0, speech rate, speaking time, and turn-taking gaps, extracted from footage recorded during a semi-structured socially interactive situation from ADOS. We calculated not only the statistic values in a whole session of the ADOS activity but also conducted a block analysis, computing the statistical values of the prosodic features in each 8s sliding window. The block analysis identified whether participants changed volume or pitch according to the flow of the conversation. We also measured the synchrony between the participant and the ADOS administrator. Participants with high-functioning ASD showed significantly longer turn-taking gaps and a greater proportion of pause time, less variability and less synchronous changes in blockwise mean of intensity compared with those with typical development (TD) (p<0.05 corrected). In addition, the ASD group had significantly wider distribution than the TD group in the within-participant variability of blockwise mean of log F0 (p<0.05 corrected). The clinical diagnosis could be discriminated using the speech features with 89% accuracy. The features of turn-taking and pausing were significantly correlated with deficits of ASD in reciprocity (p<0.05 corrected). Additionally, regression analysis provided 1.35 of mean absolute error in the prediction of deficits in reciprocity, to which the synchrony of intensity especially contributed. The findings suggest that considering variance of speech features, interaction and synchrony with conversation partner are critical to characterize atypical features in the conversation of people with ASD.