Analyzing short term dynamic speech features for understanding behavioral traits of children with autism spectrum disorder

Computational methodologies have shown promise in advancing diagnostic and intervention research in the domain of Autism Spectrum Disorder (ASD). Prior works have investigated speech features to assess disorder severity and also to differentiate between children with and without an ASD diagnosis. In this work, we explore short term dynamic functionals of speech features both within and across speakers to understand if local changes in speech provide information toward phenotyping of ASD. We compare the contributions of static and dynamic functionals representing conversational  speech toward the clinical diagnosis state. Our results show that predictions obtained from a combination of dynamic and static functionals have comparable or superior performance to the predictions obtained from just static speech functionals. We also analyze the relationship between speech production and ASD diagnosis through correlation analyses between speech functionals and manually-derived behavioral codes related to autism severity. The experimental results support the notion that dynamic speech functionals capture complementary information which can facilitate enriched analysis of clinically meaningful behavioral inference tasks.