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We propose a Reinforcement-Learning-based system that would automaticallyprescribe a hypothetical patient medication that may help the patient withtheir mental-health-related speech disfluency, and adjust the medication andthe dosages in response to zero-cost frequent measurement of the fluency of thepatient. We demonstrate the components of the system: a module that detects andevaluates speech disfluency on a large dataset we built, and a ReinforcementLearning algorithm that automatically finds good combinations of medications.To support the two modules, we collect data on the effect of psychiatricmedications for speech disfluency from the literature, and build a plausiblepatient simulation system. We demonstrate that the Reinforcement Learningsystem is, under some circumstances, able to converge to a good medicationregime. We collect and label a dataset of people with possible speechdisfluency and demonstrate our methods using that dataset. Our work is a proofof concept: we show that there is promise in the idea of using automatic datacollection to address disfluency.

Toward A Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency