We propose a Reinforcement-Learning-based system that would automatically
prescribe a hypothetical patient medications that may help the patient with
their mental-health-related speech disfluency, and adjust the medication and
the dosages in response to data from 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.

我们提出了一种基于强化学习的系统，该系统可以自动为假设的病人开处方药物，帮助病人处理与心理健康相关的语言不流畅问题，并根据病人的数据调整药物和剂量。通过一个大型数据集上建立的模块检测和评估语言不流畅，以及自动找到良好药物组合的强化学习算法，我们展示了该系统的组成部分。为了支持这两个模块，我们从文献中收集了关于心理药物对语言不流畅的效果的数据，并构建了一个合理的病人模拟系统。我们证明了在某些情况下，强化学习系统能够收敛到一个良好的用药方案。我们收集并标记了一组可能存在语言不流畅的人的数据集，并使用该数据集展示了我们的方法。我们的工作是一个概念验证：我们展示了自动数据收集在解决语言不流畅问题上的潜力。

基于强化学习的药物调整系统以最小化言语不流利

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

We present a comprehensive empirical study for personalized spontaneous
speech synthesis on the basis of linguistic knowledge. With the advent of voice
cloning for reading-style speech synthesis, a new voice cloning paradigm for
human-like and spontaneous speech synthesis is required. We, therefore, focus
on personalized spontaneous speech synthesis that can clone both the
individual's voice timbre and speech disfluency. Specifically, we deal with
filled pauses, a major source of speech disfluency, which is known to play an
important role in speech generation and communication in psychology and
linguistics. To comparatively evaluate personalized filled pause insertion and
non-personalized filled pause prediction methods, we developed a speech
synthesis method with a non-personalized external filled pause predictor
trained with a multi-speaker corpus. The results clarify the position-word
entanglement of filled pauses, i.e., the necessity of precisely predicting
positions for naturalness and the necessity of precisely predicting words for
individuality on the evaluation of synthesized speech.

本研究旨在探讨基于语言知识的个性化自发性语音合成，着重针对充满话语不流畅的填充停顿进行个性化处理，通过开发一个基于多说话者语料库训练的非个性化外部填充停顿预测器的语音合成方法进行比较评估，证明了填充停顿时态与词汇的交错对自然度和个性化的必要性。