Machine Learning to Detect Vocal Stereotypy: Improving Duration-Based Measures

Direct observation is a process central to behavior science, but its implementation may be challenging in some contexts (e.g., classrooms, homes). One potential solution to improve the feasibility of conducting behavioral observation and measurement involves machine learning. Using previously published data, 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 the original study. The next step is for researchers to test the models on novel datasets to examine the generalizability of our findings.