@inproceedings{zhang-etal-2025-soundwave,
    title = "Soundwave: Less is More for Speech-Text Alignment in {LLM}s",
    author = "Zhang, Yuhao  and
      Liu, Zhiheng  and
      Bu, Fan  and
      Zhang, Ruiyu  and
      Wang, Benyou  and
      Li, Haizhou",
    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.917/",
    pages = "18718--18738",
    ISBN = "979-8-89176-251-0",
    abstract = "Existing end-to-end speech large language models (LLMs) usually rely on large-scale annotated data for training, while data-efficient training has not been discussed in depth. We focus on two fundamental problems between speech and text: the representation space gap and sequence length inconsistency. We propose Soundwave, which utilizes an efficient training strategy and a novel architecture to address these issues. Results show that Soundwave outperforms other advanced speech LLMs in speech translation and AIR-Bench speech tasks with only a fraction of the training data. Further analysis shows that Soundwave still retains its intelligence during conversation."
}