@inproceedings{nakata-etal-2026-j,
    title = "{J}-{CHAT}: {J}apanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling",
    author = "Nakata, Wataru  and
      Seki, Kentaro  and
      Yanaka, Hitomi  and
      Saito, Yuki  and
      Takamichi, Shinnosuke  and
      Saruwatari, Hiroshi",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.lrec-1.444/",
    doi = "10.63317/2rjcajopifoh",
    pages = "5649--5656",
    abstract = "Spoken dialogue is essential for human-AI interactions, providing expressive capabilities beyond text. Developing effective spoken dialogue systems (SDSs) requires large-scale, high-quality, and diverse spoken dialogue corpora. However, existing datasets are often limited in size, spontaneity, or linguistic coherence. To address these limitations, we introduce J-CHAT, a 76,000-hour open-source Japanese spoken dialogue corpus. Constructed using an automated, language-independent methodology, J-CHAT ensures acoustic cleanliness, diversity, and natural spontaneity. The corpus is built from YouTube and podcast data, with extensive filtering and denoising to enhance quality. Experimental results with generative spoken dialogue language models trained on J-CHAT demonstrate its effectiveness for SDS development. By providing a robust foundation for training advanced dialogue models, we anticipate that J-CHAT will drive progress in human-AI dialogue research and applications."
}