@inproceedings{jung-etal-2026-sommelier,
    title = "Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models",
    author = "Jung, Kyudan  and
      Kim, Jihwan  and
      Kim, Soyoon  and
      Kim, Jeonghoon  and
      Choo, Jaegul  and
      Park, Cheonbok",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.18/",
    pages = "259--284",
    ISBN = "979-8-89176-394-4",
    abstract = "As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer interaction.However, the development of such models is constrained by the scarcity of high-quality, multi-speaker conversational data, as existing large-scale resources are predominantly single-speaker or limited in volume.Addressing the complex dynamics of natural dialogue, such as overlapping and back-channeling remains a challenge, with standard processing pipelines suffering from diarization errors and ASR hallucinations.To bridge this gap, we present a robust and scalable open-source data processing pipeline designed for full-duplex model.Our code and project page are publicly available at https://anonymous-2001-j.github.io/sommelier.github.io/."
}