Abstract
Autism spectrum disorder (ASD) is a highly-prevalent neuraldevelopmental disorder. In medical research often characterized by social communicative deficits and restricted repetitive interest. The heterogeneous nature of ASD in its behavior manifestations encompasses broad syndromes such as, Classical Autism (AD), Asperger syndrome (AS), and High functioning Autism (HFA). To evaluate the degree and there syndromes in ASD, doctor will diagnose through clinical observation and auxiliary diagnostic tools, one of them is Autism Diagnostic Observation Schedule (ADOS), i.e., a gold standard diagnostic tool. However, there are existing some problems in diagnosis of autism such as, subjective evaluation, non-scalable, and time-consuming. In this work, we design an automatic assessment system based on computing multimodal behavior features, including acoustic characteristic、body movements of the participant, using LSTM algorithm and machine learning technique to build model during ADOS story-telling part by behavioral signal processing (BSP) concept. Further, our behavior-based measurement achieve competitive, sometimes exceeding, recognition accuracies in discriminating between three syndromes of ASD when compare to investigator’s clinical-rating on participant during ADOS. Keywords: autism spectrum disorder, autism diagnostic observation schedule, long short-term memory, behavioral signal processing (BSP), multimodal behaviors