title: E3 TTS: Easy End-to-End Diffusion-based Text to Speech
summary: We propose Easy End-to-End Diffusion-based Text to Speech, a simple and
  efficient end-to-end text-to-speech model based on diffusion. E3 TTS directly
  takes plain text as input and generates an audio waveform through an iterative
  refinement process. Unlike many prior work, E3 TTS does not rely on any
  intermediate representations like spectrogram features or alignment
  information. Instead, E3 TTS models the temporal structure of the waveform
  through the diffusion process. Without relying on additional conditioning
  information, E3 TTS could support flexible latent structure within the given
  audio. This enables E3 TTS to be easily adapted for zero-shot tasks such as
  editing without any additional training. Experiments show that E3 TTS can
  generate high-fidelity audio, approaching the performance of a state-of-the-art
  neural TTS system. Audio samples are available at https://e3tts.github.io.
paper_authors._id: 654507e52a13610acc1fb6c0
paper_authors.name: Yuan Gao
paper_authors._id: 654507e52a13610acc1fb6c1
paper_authors.name: Nobuyuki Morioka
paper_authors._id: 654507e52a13610acc1fb6c2
paper_authors.name: Yu Zhang
paper_authors._id: 654507e52a13610acc1fb6c3
paper_authors.name: Nanxin Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T09:41:05.506Z
paper_authors._id: 661c2c46bb1b205d62b52ea0
paper_authors.avatarUrl: /avatars/7ca05de9fedd5d7074a1b7fdd0a47e1a.svg
paper_authors.fullname: NX Chen
paper_authors.type: user
paper_authors.user: cnxhk
paper_title: E3 TTS: Easy End-to-End Diffusion-based Text to Speech
paper_summary: We propose Easy End-to-End Diffusion-based Text to Speech, a simple and
  efficient end-to-end text-to-speech model based on diffusion. E3 TTS directly
  takes plain text as input and generates an audio waveform through an iterative
  refinement process. Unlike many prior work, E3 TTS does not rely on any
  intermediate representations like spectrogram features or alignment
  information. Instead, E3 TTS models the temporal structure of the waveform
  through the diffusion process. Without relying on additional conditioning
  information, E3 TTS could support flexible latent structure within the given
  audio. This enables E3 TTS to be easily adapted for zero-shot tasks such as
  editing without any additional training. Experiments show that E3 TTS can
  generate high-fidelity audio, approaching the performance of a state-of-the-art
  neural TTS system. Audio samples are available at https://e3tts.github.io.
paper_ai_summary: E3 TTS is a diffusion-based text-to-speech model that generates high-fidelity audio directly from text without intermediate representations or additional conditioning, supporting zero-shot tasks.
paper_ai_keywords: diffusion-based end-to-end diffusion process spectrogram features alignment information temporal structure waveform high-fidelity audio neural TTS