@inproceedings{chen-etal-2026-sac,
    title = "{SAC}: Neural Speech Codec with Semantic-Acoustic Dual-Stream Quantization",
    author = "Chen, Wenxi  and
      Yan, Ruiqi  and
      Chen, Yushen  and
      Niu, Zhikang  and
      Ma, Ziyang  and
      Li, Xiquan  and
      Liang, Yuzhe  and
      Wenhanlin  and
      Yin, Shunshun  and
      Tao, Ming  and
      Wang, Xinsheng  and
      Chen, Xie",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.138/",
    pages = "3030--3048",
    ISBN = "979-8-89176-390-6",
    abstract = "Speech codecs that convert continuous speech signals into discrete tokens have become essential for speech language models. However, existing codecs struggle to balance high-quality reconstruction with semantically rich representations, limiting their effectiveness in both generative and understanding tasks. In this work, we propose $\textbf{SAC}$, a neural speech codec with semantic-acoustic dual-stream quantization. By disentangling semantic and acoustic modeling into two dedicated streams, SAC enables each to be optimized for its respective role. Comprehensive evaluations show that SAC achieves strong reconstruction performance across diverse bitrates under both clean and noisy conditions, with particularly high scores on UTMOS and WER, indicating superior naturalness and intelligibility. Moreover, SAC substantially surpasses prior codecs in semantic representation, approaching the level of continuous self-supervised embeddings. When used as a tokenizer for LLM-based text-to-speech, SAC enables a single-stage autoregressive (AR) TTS model that clearly outperforms state-of-the-art AR systems. Our disentanglement analysis further validates the effectiveness of the dual-stream design, offering new potential for controllable speech generation."
}