Abstract
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.
Highlights
3Département de génie logiciel et des TI, École de technologie supérieure. 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
We conducted a proof of concept examining whether artificial intelligence could measure vocal stereotypy in individuals with autism
Our results showed that the artificial neural network performed adequately in measuring engagement in vocal stereotypy for 6 of 8 participants
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. If a researcher has 20 hr of video recordings to score for one participant, the human resources can add up to 30 to 50 hr of work This time does not include the resources and time involved in hiring and training additional staff to conduct these tasks. These additional resources can add up rapidly and limit the amount of research that can be done or the number of intervention sessions that can be afforded
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