Autism Spectrum Disorder’s Severity Prediction Model Using Utterance Features for Automatic Diagnosis Support

Diagnoses of autism spectrum disorder (ASD) are difficult due to difference of interviewers and environments, etc. We show relations between utterance features and ASD severity scores, which were manually given by clinical psychologists. These scores are based on the Autism Diagnostic Observation Schedule (ADOS), which is the standard metrics for symptom evaluation for subjects who are suspected as ASD. We built our original corpus where we transcribed voice records of our ADOS evaluation experiment movies. Our corpus is the world largest as speech/dialog of ASD subjects, and there has been no such ADOS corpus available in Japanese language as far as we know. We investigated relationships between ADOS scores (severity) and our utterance features, automatically estimated their scores using support vector regression (SVR). Our average estimation errors were around error rates that human ADOS experts are required not to exceed. Because our detailed analysis for each part of the ADOS test (“puzzle toy assembly + story telling” part and the “depiction of a picture” part) shows different error rates, effectiveness of our features would depend on the contents of the records. Our entire results suggest a new automatic way to assist humans’ diagnosis, which could help supporting language rehabilitation for individuals with ASD in future.