Automatic Autism Spectrum Disorder Detection Using Everyday Vocalizations Captured by Smart Devices

Autism Spectrum Disorder (ASD) is a pervasive and lifelong neuro-developmental disability where early treatment has been shown to improve a person's symptoms and ability to function. One of the most significant obstacles to effective treatment of ASD is the challenge of early detection, but unfortunately, due to the limited availability of screening and diagnostic instruments in some regions, many affected children remain undiagnosed or are diagnosed late. Recent studies have shown that characteristics in vocalizations could be used to build new ASD screening tools, but most prior efforts are based on recordings made in controlled settings and processed manually, affecting the practical value of such solutions. On the other hand, we are increasingly surrounded by smart devices that can capture an individual's vocalizations, including devices specifically targeted at child populations (e.g., Amazon Echo Kids Edition). In this paper, we propose a practical and fully automatic ASD screening solution that can be implemented on such devices, which captures and analyzes a child's everyday vocalizations at home, without the need for professional help. A 17-month experiment on 35 children is used to verify the effectiveness of the proposed approach, showing that we can obtain an unweighted F1-score of 0.87 for the classification of typically developing and ASD children.