  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.
