Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study
JMIR Pediatrics and Parenting2022Vol. 5(2), pp. e35406–e35406
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Nathan Chi, Peter Washington, Aaron Kline, Arman Husic, Cathy Hou, Chloe He, Kaitlyn Dunlap, Dennis P. Wall
Abstract
Our models were able to predict autism status when trained on a varied selection of home audio clips with inconsistent recording qualities, which may be more representative of real-world conditions. The results demonstrate that machine learning methods offer promise in detecting autism automatically from speech without specialized equipment.
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