@inproceedings{meng-etal-2025-autoregressive,
    title = "Autoregressive Speech Synthesis without Vector Quantization",
    author = "Meng, Lingwei  and
      Zhou, Long  and
      Liu, Shujie  and
      Chen, Sanyuan  and
      Han, Bing  and
      Hu, Shujie  and
      Liu, Yanqing  and
      Li, Jinyu  and
      Zhao, Sheng  and
      Wu, Xixin  and
      Meng, Helen M.  and
      Wei, Furu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.65/",
    pages = "1287--1300",
    ISBN = "979-8-89176-251-0",
    abstract = "We present MELLE, a novel continuous-valued token based language modeling approach for text-to-speech synthesis (TTS). MELLE autoregressively generates continuous mel-spectrogram frames directly from text condition, bypassing the need for vector quantization, which is typically designed for audio compression and sacrifices fidelity compared to continuous representations. Specifically, (i) instead of cross-entropy loss, we apply regression loss with a proposed spectrogram flux loss function to model the probability distribution of the continuous-valued tokens; (ii) we have incorporated variational inference into MELLE to facilitate sampling mechanisms, thereby enhancing the output diversity and model robustness. Experiments demonstrate that, compared to the two-stage codec language model VALL-E and its variants, the single-stage MELLE mitigates robustness issues by avoiding the inherent flaws of sampling vector-quantized codes, achieves superior performance across multiple metrics, and, most importantly, offers a more streamlined paradigm. The demos of our work are provided at https://aka.ms/melle."
}