Objective assessment of autism spectrum disorder based on performance in structured interpersonal acting-out tasks with prosodic stability and variability.

In this study, we sought to objectively and quantitatively characterize the prosodic features of autism spectrum disorder (ASD) via the characteristics of prosody in a newly developed structured speech experiment. Male adults with high-functioning ASD and age/intelligence-matched men with typical development (TD) were asked to read 29 brief scripts aloud in response to preceding auditory stimuli. To investigate whether (1) highly structured acting-out tasks can uncover the prosodic of difference between those with ASD and TD, and (2) the prosodic stableness and flexibleness can be used for objective automatic assessment of ASD, we compared prosodic features such as fundamental frequency, intensity, and mora duration. The results indicate that individuals with ASD exhibit stable pitch registers or volume levels in some affective vocal-expression scenarios, such as those involving anger or sadness, compared with TD and those with TD. However, unstable prosody was observed in some timing control or emphasis tasks in the participants with ASD. Automatic classification of the ASD and TD groups using a support vector machine (SVM) with speech features exhibited an accuracy of 90.4%. A machine learning-based assessment of the degree of ASD core symptoms using support vector regression (SVR) also had good performance. These results may inform the development of a new easy-to-use assessment tool for ASD core symptoms using recorded audio signals.