  In text-to-speech (TTS) synthesis, diffusion models have achieved promising
generation quality. However, because of the pre-defined data-to-noise diffusion
process, their prior distribution is restricted to a noisy representation,
which provides little information of the generation target. In this work, we
present a novel TTS system, Bridge-TTS, making the first attempt to substitute
the noisy Gaussian prior in established diffusion-based TTS methods with a
clean and deterministic one, which provides strong structural information of
the target. Specifically, we leverage the latent representation obtained from
text input as our prior, and build a fully tractable Schrodinger bridge between
it and the ground-truth mel-spectrogram, leading to a data-to-data process.
Moreover, the tractability and flexibility of our formulation allow us to
empirically study the design spaces such as noise schedules, as well as to
develop stochastic and deterministic samplers. Experimental results on the
LJ-Speech dataset illustrate the effectiveness of our method in terms of both
synthesis quality and sampling efficiency, significantly outperforming our
diffusion counterpart Grad-TTS in 50-step/1000-step synthesis and strong fast
TTS models in few-step scenarios. Project page: https://bridge-tts.github.io/
