We propose a reinforcement learning (RL)-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 an RL 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 RL 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 speech disfluency.

翻译：我们提出一种基于强化学习的系统，该系统能够自动为假设患者开具可能改善其心理健康相关言语不流畅的药物，并通过零成本的频繁言语流畅性测量来调整药物及其剂量。我们展示了该系统的组成部分：一个在我们构建的大规模数据集上检测和评估言语不流畅的模块，以及一个自动寻找良好药物组合的强化学习算法。为支撑这两个模块，我们从文献中收集精神科药物对言语不流畅影响的数据，并构建一个合理的患者模拟系统。我们证明，在某些情况下，强化学习系统能够收敛到良好的用药方案。我们收集并标注了一个可能存在言语不流畅的人群数据集，并利用该数据集演示了我们的方法。本研究为概念验证：我们展示了利用自动数据收集来应对言语不流畅这一思路的潜力。