dc.contributor.author | Dufour, Marie-Michèle | |
dc.contributor.author | Lanovaz, Marc | |
dc.contributor.author | Cardinal, Patrick | |
dc.date.accessioned | 2020-11-30T13:19:44Z | |
dc.date.available | NO_RESTRICTION | fr |
dc.date.available | 2020-11-30T13:19:44Z | |
dc.date.issued | 2020-11-03 | |
dc.identifier.uri | http://hdl.handle.net/1866/24064 | |
dc.publisher | Wiley | fr |
dc.rights | Il s'agit d'un article en libre accès selon les termes de la
Creative Commons Attribution License, qui permet
l'utilisation, la distribution et la reproduction sur tout support, à condition que l'œuvre originale soit correctement citée. | |
dc.rights | Attribution 4.0 International (CC BY 4.0) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/deed.fr | |
dc.subject | Artificial intelligence | fr |
dc.subject | Artificial neural network | fr |
dc.subject | Autism measurement | fr |
dc.subject | Stereotypy | fr |
dc.title | Artificial intelligence for the measurement of vocal stereotypy | fr |
dc.type | Article | fr |
dc.contributor.affiliation | Université de Montréal. Faculté des arts et des sciences. École de psychoéducation | fr |
dc.identifier.doi | 10.1002/jeab.636 | |
dcterms.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. | fr |
dcterms.isPartOf | urn:ISSN:0022-5002 | fr |
dcterms.isPartOf | urn:ISSN:1938-3711 | fr |
dcterms.language | eng | fr |
dcterms.relation | https://osf.io/e4vbs/ | fr |
UdeM.ReferenceFournieParDeposant | Dufour, M.-M., Lanovaz, M. J., Cardinal, P. (2020). Artificial intelligence for the measurement of vocal stereotypy. Journal of Experimental Analysis of Behavior, 114(3), 368-380. https://doi.org/10.1002/jeab.636 | fr |
UdeM.VersionRioxx | Version publiée / Version of Record | fr |
oaire.citationTitle | Journal of the experimental analysis of behavior | fr |
oaire.citationVolume | 114 | fr |
oaire.citationIssue | 3 | fr |
oaire.citationStartPage | 368 | fr |
oaire.citationEndPage | 380 | fr |