Emotional text-to-speech synthesis (ETTS) has seen much progress in recent
years. However, the generated voice is often not perceptually identifiable by
its intended emotion category. To address this problem, we propose a new
interactive training paradigm for ETTS, denoted as i-ETTS, which seeks to
directly improve the emotion discriminability by interacting with a speech
emotion recognition (SER) model. Moreover, we formulate an iterative training
strategy with reinforcement learning to ensure the quality of i-ETTS
optimization. Experimental results demonstrate that the proposed i-ETTS
outperforms the state-of-the-art baselines by rendering speech with more
accurate emotion style. To our best knowledge, this is the first study of
reinforcement learning in emotional text-to-speech synthesis.

本研究提出一个新的交互式训练范式（i-ETTS）以及一种迭代的强化学习训练策略，应用于情感文本朗读合成 (ETTS) 任务，此方法以情感识别为指导，提高情感可辨别性，在清楚地传达出文字情感的同时，提供更加准确的情感朗读效果，实验结果表明此方法优于当前最先进的基线方法，且是首次在情感文本朗读合成任务中引入强化学习技术。