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arXiv · 2508.08550

Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization

Chaoqun Cui·Liangbin Huang·Shijing Wang·Zhe Tong·Zhaolong Huang·Xiao Zeng·Xiaofeng Liu
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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.

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Chaoqun Cui, Liangbin Huang, Shijing Wang, Zhe Tong, Zhaolong Huang, Xiao Zeng, Xiaofeng Liu. 2025-08-12. Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization. https://doi.org/10.18653/v1%2F2025.acl-long.227

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