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.

翻译：自闭症谱系障碍（ASD）的诊断是一项复杂且具有挑战性的任务，因为它依赖于心理学家对互动行为的分析，而非生化诊断手段。本文提出了一种基于ASD诊断的建模方法，通过分析心理学家与典型发育（TD）儿童或ASD儿童之间的诊断对话中提取的声学/韵律及语言特征。我们比较了不同特征在不同对话任务中的贡献，重点关注寻找能够表征ASD儿童对话行为的最小参数集合。由于ASD通过对话互动进行诊断，除了分析儿童的行为外，我们还探究了心理学家的对话行为是否因诊断组别而异。本研究结果有助于对ASD儿童对话数据进行细粒度分析，从而支持诊断与干预。