E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS
<|reference_start|>E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS: This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully non-autoregressive zero-shot text-to-speech system that offers human-level naturalness and state-of-the-art speaker similarity and intelligibility. In the E2 TTS framework, the text input is converted into a character sequence with filler tokens. The flow-matching-based mel spectrogram generator is then trained based on the audio infilling task. Unlike many previous works, it does not require additional components (e.g., duration model, grapheme-to-phoneme) or complex techniques (e.g., monotonic alignment search). Despite its simplicity, E2 TTS achieves state-of-the-art zero-shot TTS capabilities that are comparable to or surpass previous works, including Voicebox and NaturalSpeech 3. The simplicity of E2 TTS also allows for flexibility in the input representation. We propose several variants of E2 TTS to improve usability during inference. See https://aka.ms/e2tts/ for demo samples.<|reference_end|>
@article{eskimez2024e2,
    title={E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS},
    author={Sefik Emre Eskimez, Xiaofei Wang, Manthan Thakker, Canrun Li,
  Chung-Hsien Tsai, Zhen Xiao, Hemin Yang, Zirun Zhu, Min Tang, Xu Tan, Yanqing
  Liu, Sheng Zhao, Naoyuki Kanda},
    journal={arXiv preprint arXiv:2406.18009},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18009},
    primaryClass={eess.AS cs.SD}
}