title: Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis
summary: 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/
paper_authors._id: 65713f7ad877298e1d5379a1
paper_authors.name: Zehua Chen
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/d63fb9ce5e97e2c77e500b329aeec8b0.svg
paper_authors.fullname: Zehua Chen
paper_authors.type: user
paper_authors.user: ZehuaChenICL
paper_authors._id: 65713f7ad877298e1d5379a2
paper_authors.name: Guande He
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/7ad03e55a8791c62f1271a5c9bf8cc60.svg
paper_authors.fullname: Guande He
paper_authors.type: user
paper_authors.user: gdhe17
paper_authors._id: 65713f7ad877298e1d5379a3
paper_authors.name: Kaiwen Zheng
paper_authors.status: claimed_verified
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paper_authors._id: 652bf7edc3cba555d5673c6e
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paper_authors.fullname: Kaiwen Zheng
paper_authors.type: user
paper_authors.user: worstcoder
paper_authors._id: 65713f7ad877298e1d5379a4
paper_authors.name: Xu Tan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-07T10:55:39.819Z
paper_authors._id: 5f1040b6e9d71719e3be71d2
paper_authors.avatarUrl: /avatars/a2f28940236ae625ed3810ad62e343ff.svg
paper_authors.fullname: Xu Tan
paper_authors.type: user
paper_authors.user: xutan
paper_authors._id: 65713f7ad877298e1d5379a5
paper_authors.name: Jun Zhu
paper_title: Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis
paper_summary: 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/
paper_ai_summary: Bridge-TTS introduces a novel text-to-speech system using a clean deterministic prior in diffusion models, significantly improving synthesis quality and sampling efficiency over existing methods.
paper_ai_keywords: diffusion models noisy Gaussian prior clean and deterministic prior latent representation Schrodinger bridge ground-truth mel-spectrogram data-to-data process noise schedules stochastic samplers deterministic samplers LJ-Speech dataset Grad-TTS