  Although current Text-To-Speech (TTS) models are able to generate
high-quality speech samples, there are still challenges in developing emotion
intensity controllable TTS. Most existing TTS models achieve emotion intensity
control by extracting intensity information from reference speeches.
Unfortunately, limited by the lack of modeling for intra-class emotion
intensity and the model's information decoupling capability, the generated
speech cannot achieve fine-grained emotion intensity control and suffers from
information leakage issues. In this paper, we propose an emotion transfer TTS
model, which defines a remapping-based sorting method to model intra-class
relative intensity information, combined with Mutual Information (MI) to
decouple speaker and emotion information, and synthesizes expressive speeches
with perceptible intensity differences. Experiments show that our model
achieves fine-grained emotion control while preserving speaker information.
