Parameter Selection for Analyzing Conversations with Autism Spectrum Disorder
This work addresses the challenge of ASD diagnosis for psychologists and clinicians by providing a data-driven modeling approach to support diagnosis and intervention, though it appears incremental as it builds on existing feature analysis methods.
The paper tackled the problem of diagnosing autism spectrum disorder (ASD) by analyzing acoustic/prosodic and linguistic features from conversations between psychologists and children, identifying a minimal set of parameters to characterize ASD conversational behaviors and showing that psychologist behaviors also vary across groups.
The diagnosis of autism spectrum disorder (ASD) is a complex, challenging task as it depends on the analysis of interactional behaviors by psychologists rather than the use of biochemical diagnostics. In this paper, we present a modeling approach to ASD diagnosis by analyzing acoustic/prosodic and linguistic features extracted from diagnostic conversations between a psychologist and children who either are typically developing (TD) or have ASD. We compare the contributions of different features across a range of conversation tasks. We focus on finding a minimal set of parameters that characterize conversational behaviors of children with ASD. Because ASD is diagnosed through conversational interaction, in addition to analyzing the behavior of the children, we also investigate whether the psychologist's conversational behaviors vary across diagnostic groups. Our results can facilitate fine-grained analysis of conversation data for children with ASD to support diagnosis and intervention.