Machine Learning to Detect Vocal Stereotypy: Improving Duration-Based Measures Article Swipe
Ali Reza Omrani
,
Marc J. Lanovaz
,
Davide Moroni
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/c4k98
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/c4k98
Direct observation is a process central to behavior science, but its implementation occasionally requires hiring observers dedicated exclusively to data collection, which may increase the cost of providing services and conducting research. One potential solution to reduce resources necessary to conduct behavioral observation and measurement involves machine learning. Using data previously published by Dufour et al. (2020), we developed and tested novel models to automatically measure the duration of vocal stereotypy in eight children with autism. In addition to accuracy and the kappa statistic, we examined session-by-session correlations between values measured by machine learning and those recorded by a human observer. Nearly all our models produced high correlations (i.e., .90 or more) and resulted in better metrics than those reported by Dufour et al. (2020). The next step is for researchers to test the models on novel datasets to examine the generalizability of our findings.
Related Topics
Concepts
Session (web analytics)
Stereotypy
Measure (data warehouse)
Observer (physics)
Computer science
Process (computing)
Autism
Artificial intelligence
Psychology
Machine learning
Data collection
Extension (predicate logic)
Developmental psychology
Statistics
Data mining
Mathematics
Programming language
Amphetamine
Quantum mechanics
Dopamine
World Wide Web
Operating system
Physics
Neuroscience
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31234/osf.io/c4k98
- https://osf.io/c4k98/download
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401628563
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401628563Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31234/osf.io/c4k98Digital Object Identifier
- Title
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Machine Learning to Detect Vocal Stereotypy: Improving Duration-Based MeasuresWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-16Full publication date if available
- Authors
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Ali Reza Omrani, Marc J. Lanovaz, Davide MoroniList of authors in order
- Landing page
-
https://doi.org/10.31234/osf.io/c4k98Publisher landing page
- PDF URL
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https://osf.io/c4k98/downloadDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://osf.io/c4k98/downloadDirect OA link when available
- Concepts
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Session (web analytics), Stereotypy, Measure (data warehouse), Observer (physics), Computer science, Process (computing), Autism, Artificial intelligence, Psychology, Machine learning, Data collection, Extension (predicate logic), Developmental psychology, Statistics, Data mining, Mathematics, Programming language, Amphetamine, Quantum mechanics, Dopamine, World Wide Web, Operating system, Physics, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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