Bailing-TTS: Chinese Dialectal Speech Synthesis Towards Human-like Spontaneous Representation
<|reference_start|>Bailing-TTS: Chinese Dialectal Speech Synthesis Towards Human-like Spontaneous Representation: Large-scale text-to-speech (TTS) models have made significant progress recently.However, they still fall short in the generation of Chinese dialectal speech. Toaddress this, we propose Bailing-TTS, a family of large-scale TTS models capable of generating high-quality Chinese dialectal speech. Bailing-TTS serves as a foundation model for Chinese dialectal speech generation. First, continual semi-supervised learning is proposed to facilitate the alignment of text tokens and speech tokens. Second, the Chinese dialectal representation learning is developed using a specific transformer architecture and multi-stage training processes. With the proposed design of novel network architecture and corresponding strategy, Bailing-TTS is able to generate Chinese dialectal speech from text effectively and efficiently. Experiments demonstrate that Bailing-TTS generates Chinese dialectal speech towards human-like spontaneous representation. Readers are encouraged to listen to demos at \url{https://c9412600.github.io/bltts_tech_report/index.html}.<|reference_end|>
@article{di2024bailing-tts:,
    title={Bailing-TTS: Chinese Dialectal Speech Synthesis Towards Human-like
  Spontaneous Representation},
    author={Xinhan Di, Zihao Chen, Yunming Liang, Junjie Zheng, Yihua Wang,
  Chaofan Ding},
    journal={arXiv preprint arXiv:2408.00284},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00284},
    primaryClass={cs.CL cs.SD eess.AS}
}