@inproceedings{takeda-komatani-2026-retrospective,
    title = "Retrospective Speech Recognition for Spoken Dialogue System: Exploiting Subsequent Utterances to Enhance {ASR} Performance",
    author = "Takeda, Ryu  and
      Komatani, Kazunori",
    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-1.20/",
    pages = "182--192",
    abstract = "Spoken dialogue systems would benefit from the ability of self-correction, namely, {--}revising earlier recognition results once later utterances are available, as humans often do in dialogue. However, conventional automatic speech recognition ({ASR}) frameworks mainly process user utterances sequentially and rely only on the preceding context. To address this limitation, we propose Retrospective Speech Recognition ({RSR}), which refines past recognition results by exploiting its subsequent utterances. We formulate and implement an {RSR} model for a dialogue system situation where system utterances can also be utilized. Each past user utterance is processed with an interpretable syllabogram representation, which integrates preceding and subsequent utterances within a shared domain between the signal and text levels. This intermediate representation also helps reduce orthographic inconsistencies. Experimental results using real {J}apanese dialogue speech showed that utilizing the subsequent utterances improved the character error rate by 0.10 points, which demonstrates the utility of {RSR}. We also investigated the impact of other factors, such as utilization of system utterances."
}