Many frameworks for emotional text-to-speech (E-TTS) rely on human-annotated
emotion labels that are often inaccurate and difficult to obtain. Learning
emotional prosody implicitly presents a tough challenge due to the subjective
nature of emotions. In this study, we propose a novel approach that leverages
text awareness to acquire emotional styles without the need for explicit
emotion labels or text prompts. We present TEMOTTS, a two-stage framework for
E-TTS that is trained without emotion labels and is capable of inference
without auxiliary inputs. Our proposed method performs knowledge transfer
between the linguistic space learned by BERT and the emotional style space
constructed by global style tokens. Our experimental results demonstrate the
effectiveness of our proposed framework, showcasing improvements in emotional
accuracy and naturalness. This is one of the first studies to leverage the
emotional correlation between spoken content and expressive delivery for
emotional TTS.

设计了一个新颖的两阶段框架 TEMOTTS，用于基于文本的情感语音合成任务，该框架在没有情感标签和文本提示的情况下进行训练，并能够无需辅助输入进行推理。利用 BERT 学习的语言空间和全局风格标记构建的情感风格空间之间进行知识转移，实验证明了该框架的有效性，提高了情感准确性和自然度。这是第一个充分利用口头内容与表现风格之间情感相关性的情感语音合成研究之一。