  With the demand for autonomous control and personalized speech generation,
the style control and transfer in Text-to-Speech (TTS) is becoming more and
more important. In this paper, we propose a new TTS system that can perform
style transfer with interpretability and high fidelity. Firstly, we design a
TTS system that combines variational autoencoder (VAE) and diffusion refiner to
get refined mel-spectrograms. Specifically, a two-stage and a one-stage system
are designed respectively, to improve the audio quality and the performance of
style transfer. Secondly, a diffusion bridge of quantized VAE is designed to
efficiently learn complex discrete style representations and improve the
performance of style transfer. To have a better ability of style transfer, we
introduce ControlVAE to improve the reconstruction quality and have good
interpretability simultaneously. Experiments on LibriTTS dataset demonstrate
that our method is more effective than baseline models.
