Artificial intelligence for the measurement of vocal stereotypy

Both researchers and practitioners often rely on direct observation to measure and monitor behavior. When these behaviors are too complex or numerous to be measured in vivo, relying on direct observation using human observers increases the amount of resources required to conduct research and to moni...

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Bibliographic Details
Published inJournal of the experimental analysis of behavior Vol. 114; no. 3; pp. 368 - 380
Main Authors Dufour, Marie‐Michèle, Lanovaz, Marc J., Cardinal, Patrick
Format Journal Article
LanguageEnglish
Published Hoboken, USA Wiley Subscription Services, Inc 01.11.2020
Subjects
artificial intelligence
artificial neural network
autism
measurement
stereotypy
artificial neural network
artificial intelligence
autism
measurement
stereotypy
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Summary:Both researchers and practitioners often rely on direct observation to measure and monitor behavior. When these behaviors are too complex or numerous to be measured in vivo, relying on direct observation using human observers increases the amount of resources required to conduct research and to monitor the effects of interventions in practice. To address this issue, we conducted a proof of concept examining whether artificial intelligence could measure vocal stereotypy in individuals with autism. More specifically, we used an artificial neural network with over 1,500 minutes of audio data from 8 different individuals to train and test models to measure vocal stereotypy. Our results showed that the artificial neural network performed adequately (i.e., session‐by‐session correlation near or above .80 with a human observer) in measuring engagement in vocal stereotypy for 6 of 8 participants. Additional research is needed to further improve the generalizability of the approach.
ISSN:0022-5002
1938-3711
DOI:10.1002/jeab.636