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：本文提出一种基于强化学习的系统，该系统可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，并通过零成本的言语流畅性频繁测量，实时调整药物种类及剂量。我们展示了系统的核心模块：一是基于自建大规模数据集进行言语不流畅检测与评估的模块，二是可自动发现最优药物组合的强化学习算法。为支撑这两个模块，我们从文献中收集了精神类药物对言语不流畅影响的效应数据，并构建了合理的人体仿真系统。实验证明，该强化学习系统在某些条件下能够收敛至有效的用药方案。我们收集并标注了可能存在言语不流畅人群的数据集，并基于该数据集验证了方法的有效性。本研究作为概念验证表明：利用自动化数据采集解决言语不流畅问题具有可行性。