@inproceedings{obi-etal-2026-reproducing,
    title = "Reproducing Proficiency-Conditioned Dialogue Features with Full-duplex Spoken Dialogue Models",
    author = "Obi, Takao  and
      Yoshikawa, Sadahiro  and
      Saeki, Mao  and
      Eguchi, Masaki  and
      Matsuyama, Yoichi",
    editor = "Riccardi, Giuseppe  and
      Mousavi, Seyed Mahed  and
      Torres, Maria Ines  and
      Yoshino, Koichiro  and
      Callejas, Zoraida  and
      Chowdhury, Shammur Absar  and
      Chen, Yun-Nung  and
      Bechet, Frederic  and
      Gustafson, Joakim  and
      Damnati, G{\'e}raldine  and
      Papangelis, Alex  and
      D{'}Haro, Luis Fernando  and
      Mendon{\c{c}}a, John  and
      Bernardi, Raffaella  and
      Hakkani-Tur, Dilek  and
      Di Fabbrizio, Giuseppe {''}Pino{''}  and
      Kawahara, Tatsuya  and
      Alam, Firoj  and
      Tur, Gokhan  and
      Johnston, Michael",
    booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
    month = feb,
    year = "2026",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard-stats/2026.iwsds-1.4/",
    pages = "43--51",
    abstract = "Real-time, human-centered conversational {AI} requires systems that handle spoken dialogue with overlap and rapid turn-taking. Although full-duplex models promise these capabilities, empirical work applying them to conversational {AI} is still nascent. To fill this gap, this study investigates whether the full-duplex model can reproduce the human dialogue features. We adapt a full-duplex spoken dialogue model to a large corpus of second-language ({L}2) learner interviews and train proficiency-conditioned models. We then conduct real-time interview sessions between these models and a spoken dialogue system designed to elicit spontaneous learner speech, and analyze reaction time, response frequency, and fluency metrics across aggregated {CEFR} levels (A/{B}/{C}). Our results show that proficiency-conditioned models partially reproduce levelwise trends and distributions observed in human interviews across multiple metrics. These findings suggest that full-duplex models can reproduce dialogue features of human dialogues and offer a promising foundation for conversational {AI} systems."
}