  Autism spectrum disorder (ASD) is a neurodevelopmental disorder which results
in altered behavior, social development, and communication patterns. In past
years, autism prevalence has tripled, with 1 in 54 children now affected. Given
that traditional diagnosis is a lengthy, labor-intensive process, significant
attention has been given to developing systems that automatically screen for
autism. Prosody abnormalities are among the clearest signs of autism, with
affected children displaying speech idiosyncrasies including echolalia,
monotonous intonation, atypical pitch, and irregular linguistic stress
patterns. In this work, we present a suite of machine learning approaches to
detect autism in self-recorded speech audio captured from autistic and
neurotypical (NT) children in home environments. We consider three methods to
detect autism in child speech: first, Random Forests trained on extracted audio
features (including Mel-frequency cepstral coefficients); second, convolutional
neural networks (CNNs) trained on spectrograms; and third, fine-tuned wav2vec
2.0--a state-of-the-art Transformer-based ASR model. We train our classifiers
on our novel dataset of cellphone-recorded child speech audio curated from
Stanford's Guess What? mobile game, an app designed to crowdsource videos of
autistic and neurotypical children in a natural home environment. The Random
Forest classifier achieves 70% accuracy, the fine-tuned wav2vec 2.0 model
achieves 77% accuracy, and the CNN achieves 79% accuracy when classifying
children's audio as either ASD or NT. Our models were able to predict autism
status when training on a varied selection of home audio clips with
inconsistent recording quality, which may be more generalizable to real world
conditions. These results demonstrate that machine learning methods offer
promise in detecting autism automatically from speech without specialized
equipment.
