Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study

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Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study
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Smartphone game data could facilitate earlier diagnosis of autismspectrumdisorder and improve treatment

Audio Data”). During a game session, parents and children choose either a charades game or a simple quiz game . Children are directed to follow the rules of gameplay, while parents serve as game mediators. Throughout the session, parents record their children by placing their smartphones on their foreheads with the front-facing camera oriented toward the child.

crowdsources the recording of videos of NT children and children with ASD from consenting participants. Audio of children's speech is manually spliced from the videos and 3 models are trained on this audio data. The first is a random forest classifier, which uses an ensemble of independently trained decision trees. The second is a CNN. The third is a fine-tuned wav2vec 2.0 model.

Table 1. Distribution of 850 audio clips across 5 folds. Each of the 3 models was trained on the same distribution of clips with 5-fold cross-validation.

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