@inproceedings{kim-etal-2026-two,
    title = "Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses",
    author = "Kim, Sungnyun  and
      Jang, Kangwook  and
      Cho, Sungwoo  and
      Chung, Joon Son  and
      Kim, Hoi-Rin  and
      Yun, Se-Young",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.26/",
    pages = "544--564",
    ISBN = "979-8-89176-395-1",
    abstract = "This paper introduces a new paradigm for generative error correction (GER) framework in audio-visual speech recognition (AVSR) that reasons over modality-specific evidences directly in the language space. Our framework, **DualHyp**, empowers a large language model (LLM) to compose independent $N$-best hypotheses from separate automatic speech recognition (ASR) and visual speech recognition (VSR) models. To maximize the effectiveness of DualHyp, we further introduce **RelPrompt**, a noise-aware guidance mechanism that provides modality-grounded prompts to the LLM. RelPrompt offers the temporal reliability of each modality stream, guiding the model to dynamically switch its focus between ASR and VSR hypotheses for an accurate correction. Under various corruption scenarios, our framework attains up to 57.7{\%} error rate gain on the LRS2 benchmark over standard ASR baseline, contrary to single-stream GER approaches that achieve only 10{\%} gain. To facilitate research within our DualHyp framework, we release the code and the dataset comprising ASR and VSR hypotheses at https://github.com/sungnyun/dualhyp."
}