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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wiley Subscription Services, Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756764/ https://www.ncbi.nlm.nih.gov/pubmed/33145781 http://dx.doi.org/10.1002/jeab.636 |
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author | Dufour, Marie‐Michèle Lanovaz, Marc J. Cardinal, Patrick |
author_facet | Dufour, Marie‐Michèle Lanovaz, Marc J. Cardinal, Patrick |
author_sort | Dufour, Marie‐Michèle |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7756764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Wiley Subscription Services, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77567642020-12-28 Artificial intelligence for the measurement of vocal stereotypy Dufour, Marie‐Michèle Lanovaz, Marc J. Cardinal, Patrick J Exp Anal Behav Research Articles 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. Wiley Subscription Services, Inc. 2020-11-03 2020-11 /pmc/articles/PMC7756764/ /pubmed/33145781 http://dx.doi.org/10.1002/jeab.636 Text en © 2020 The Authors. Journal of the Experimental Analysis of Behavior published by Wiley Periodicals LLC on behalf of Society for the Experimental Analysis of Behavior. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Dufour, Marie‐Michèle Lanovaz, Marc J. Cardinal, Patrick Artificial intelligence for the measurement of vocal stereotypy |
title | Artificial intelligence for the measurement of vocal stereotypy |
title_full | Artificial intelligence for the measurement of vocal stereotypy |
title_fullStr | Artificial intelligence for the measurement of vocal stereotypy |
title_full_unstemmed | Artificial intelligence for the measurement of vocal stereotypy |
title_short | Artificial intelligence for the measurement of vocal stereotypy |
title_sort | artificial intelligence for the measurement of vocal stereotypy |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756764/ https://www.ncbi.nlm.nih.gov/pubmed/33145781 http://dx.doi.org/10.1002/jeab.636 |
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