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

Abstract

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.

Distinctive prosodic features of people with autism spectrum disorder: a systematic review and meta-analysis study

“…Sheinkopf et al, investigating the acoustic characteristics of infants’ crying sound reported two mean pitch values for pain-related and non-pain-related cries. Furthermore, 11 studies 13 , 15 , 18 , 29 , 38 , 45 , 49 , 58 – 60 , 63 found no significant difference (p > 0.05) between groups. By analyzing adult male participants (age > 13), Nayak et al found a higher mean pitch value for ASD than TD group, while for younger male participants (age < 13), the mean pitch value was not different between groups 38 .…”

“…The difference in the intensity of vocal productions between ASD and TD individuals was found in 12 studies. Two studies 21 , 23 reported higher intensity for ASD individuals, while two others 58 , 60 found the lower intensity for ASD individuals. In addition, there was not any report of significant differences between ASD and TD groups in terms of voice intensity level in eight studies 10 , 17 , 22 , 29 , 36 , 59 , 62 , 63 .…”

Cortical Activation during Cooperative and Competitive Joint Actions in Children With and Without Autism Spectrum Disorder (ASD) – An fNIRS Study

“…Children with ASD also showed di culties during turn-taking tasks [39][40] . During back-and-forth conversations, children with ASD showed longer turntaking gaps and reduced temporal variability, suggesting poor response inhibition/executive functioning 39 . During cooperative/competitive joint actions, children with ASD may have di culties inferring intentions of others which might affect their joint action performance 40 .…”

“…These di culties have been attributed to their poor visuomotor and inter-limb coordination within solo and social contexts [5][6]9 . Children with ASD also showed di culties during turn-taking tasks [39][40] . During back-and-forth conversations, children with ASD showed longer turntaking gaps and reduced temporal variability, suggesting poor response inhibition/executive functioning 39 .…”

Classifying Conversational Entrainment of Speech Behavior: An Expanded Framework and Review

“…Others have found the mean feature values across a specific unit of time (e.g., minutes; Pardo et al, 2010, Gordan et al, 2015. A more complex way to capture global entrainment uses time-aligned moving averages (TAMA; e.g., Kousidis et al, 2009;De Looze et al, 2014;Ochi et al, 2019). This method involves analyzing speech segments of overlapping windows of predetermined, fixed lengths.…”