Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is prevalent and heterogeneous. Autistic traits describe a wide heterogeneity of behavior symptoms of ASD, and these traits are reflections of core neurodevelopment function deficits. Researchers have predominantly taken a clinical angle to understand autistic traits. They have been developing various clinical-grade instruments with behavioral codes to quantify autistic traits for diagnostic and research purposes. However, the need for highly trained professionals and the inevitable subjectivity limit their usage. Hence, researchers have been developing computational methods to address these issues. Among many efforts, methods based on computing speech have emerged rapidly due to their ability to characterize communicative behaviors and social interactions. Our work addresses one particular under-studied speech aspect: articulation-related acoustics, one of the broad autism spectrum symptoms. In this paper, we examine the articulatory information in a natural spoken interaction through measures of vowel space characteristics (VSCs) to understand autistic traits. Specifically, we approach by modeling statistical relationships of the corner vowel distributions and the interpersonal correlation of these relationships in conversation.
Our method is evaluated by deriving VSC features and using them in ASD classification and regression tasks. We found these features predict autism-related communication assessment and add additional information to classification tasks. Furthermore, our analyses show a relationship between VSCs and autismrelated communication deficit and also imply differences in VSCs between typical developing people and each ASD subgroup.