- 一般社団法人 人工知能学会
- 人工知能学会論文誌 (ISSN:13460714)
- vol.35, no.3, pp.B-J45_1-11, 2020-05-01 (Released:2020-05-01)
In recent years, population with autism spectrum disorder (ASD) are growing explosively, and diagnosis of ASD is difficult due to difference of interviewers and environments, etc. We show relations between utterance features and ASD severity scores, which were manually given by a clinical psychologist. These scores are of the Autism Diagnostic Observation Schedule (ADOS), which is one of 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 utterance features we defined. Our system 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. By comparing an ADOS score prediction result of adults and adults with that of children, we showed common features of ADOS scores between children and adults. Our entire results suggest a new automatic way to assist humans’ diagnosis, which could help supporting language rehabilitation for patients with ASD in future.