E1 TTS: Simple and Fast Non-Autoregressive TTS
This paper introduces Easy One-Step Text-to-Speech (E1 TTS), an efficient non-autoregressive zero-shot text-to-speech system based on denoising diffusion pretraining and distribution matching distillation. The training of E1 TTS is straightforward; it does not require explicit monotonic alignment between the text and audio pairs. The inference of E1 TTS is efficient, requiring only one neural network evaluation for each utterance. Despite its sampling efficiency, E1 TTS achieves naturalness and speaker similarity comparable to various strong baseline models. Audio samples are available at e1tts.github.io.
Authors
- Pengcheng Zhu (ORCID: https://orcid.org/0000-0002-8360-2327)
- Haizhou Li
- Mengxiao Bi (ORCID: https://orcid.org/0009-0007-6680-481X)
- Shuai Wang (ORCID: https://orcid.org/0000-0001-6457-9478)
- Zhijun Liu (ORCID: https://orcid.org/0000-0002-4679-5123)
Institutions
- NetEase (China) (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
Publication Details
- Published
- 2025-03-12
- DOI
- https://doi.org/10.1109/icassp49660.2025.10890230
- Citations
- 3
- Primary Topic
- Advanced Chemical Sensor Technologies
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Natural Science Foundation of China