ArticleFrontiers in psychiatry2023
Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model.
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Prediction models for highly scalable technology-assisted differential diagnostics of autism spectrum disorder.BMJ mental health · 2026Article
- Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder.Algorithms · 2025Article
- Temporal Predictions in Music and Language: The Case of Autism Spectrum Disorder.Annals of the New York Academy of Sciences · 2025Review
- Attenuated behavioral interpersonal synchrony in autistic adults is not explained by perception of timing.Scientific reports · 2025Article
- The influence of interpersonal synchrony and autism on impressions of dyadic interactions: a preregistered study.Molecular autism · 2025Article
- Latest clinical frontiers related to autism diagnostic strategies.Cell reports. Medicine · 2025Review
- Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews.Scientific reports · 2024Article
- Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis.Behavioral sciences (Basel, Switzerland) · 2024Review
- The role of interpersonal synchrony in forming impressions of autistic and non-autistic adults.Scientific reports · 2023Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) is diagnosed on the basis of speech and communication differences, amongst other symptoms. Since conversations are essential for building connections with others, it is important to understand the exact nature of differences between autistic and non-autistic verbal behaviour and evaluate the potential of these differences for diagnostics. In this study, we recorded dyadic conversations and used automated extraction of speech and interactional turn-taking features of 54 non-autistic and 26 autistic participants. The extracted speech and turn-taking parameters showed high potential as a diagnostic marker. A linear support vector machine was able to predict the dyad type with 76.2% balanced accuracy (sensitivity: 73.8%, specificity: 78.6%), suggesting that digitally assisted diagnostics could significantly enhance the current clinical diagnostic process due to their objectivity and scalability. In group comparisons on the individual and dyadic level, we found that autistic interaction partners talked slower and in a more monotonous manner than non-autistic interaction partners and that mixed dyads consisting of an autistic and a non-autistic participant had increased periods of silence, and the intensity, i.e. loudness, of their speech was more synchronous.
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