@inproceedings{cui-etal-2025-fine,
    title = "Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization",
    author = "Cui, Chaoqun  and
      Huang, Liangbin  and
      Wang, Shijing  and
      Tong, Zhe  and
      Huang, Zhaolong  and
      Zeng, Xiao  and
      Liu, Xiaofeng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.227/",
    pages = "4524--4546",
    ISBN = "979-8-89176-251-0",
    abstract = "Video dubbing aims to translate original speech in visual media programs from the source language to the target language, relying on neural machine translation and text-to-speech technologies. Due to varying information densities across languages, target speech often mismatches the source speech duration, causing audio-video synchronization issues that significantly impact viewer experience. In this study, we approach duration alignment in LLM-based video dubbing machine translation as a preference optimization problem. We propose the Segment Supervised Preference Optimization (SSPO) method, which employs a segment-wise sampling strategy and fine-grained loss to mitigate duration mismatches between source and target lines. Experimental results demonstrate that SSPO achieves superior performance in duration alignment tasks."
}