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.

我们通过分析从心理学家与典型发育或患有自闭症谱系障碍（ASD）的儿童之间的诊断对话中提取的声学 / 韵律和语言特征，提出了一种 ASD 诊断的建模方法。我们比较不同特征在一系列对话任务中的贡献，并着重寻找表征患有 ASD 的儿童对话行为的最小参数集。除了分析儿童的行为外，我们还研究心理学家的对话行为是否在不同的诊断群体之间有所变化。我们的结果有助于对 ASD 儿童的对话数据进行细粒度分析，以支持诊断和干预。