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

翻译：我们提出了一种基于强化学习的系统，该系统可为假设性患者自动开具可能改善其心理健康相关言语不流畅的药物处方，并通过零成本高频流利度测量实时调整药物及剂量。我们展示了系统的两个核心模块：其一是基于自建大规模数据集检测与评估言语不流畅的模块；其二是通过强化学习算法自动寻找有效药物组合的模块。为支撑这两个模块，我们系统整理了文献中精神类药物对言语不流畅疗效的数据，并构建了合理的患者模拟系统。实验表明：在某些条件下，该强化学习系统能够收敛至有效的用药方案。我们采集并标注了可能存在言语不流畅的人群数据集，并基于该数据集验证了所提方法。本研究作为概念验证工作，证明了利用自动化数据采集解决言语不流畅问题的潜力。