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arXiv · 2511.09232

POTSA: A Cross-Lingual Speech Alignment Framework for Speech-to-Text Translation

Xuanchen Li·Chenrui Cui·Tianrui Wang·Meng Ge·Zikang Huang·Yizhou Peng·Jin Li·Yuheng Lu·Yu Jiang·Nyima Tashi·Longbiao Wang·Jianwu Dang
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Abstract

Speech Large Language Models have achieved breakthroughs in multilingual speech-to-text translation. However, existing approaches often overlook semantic commonalities across source languages, leading to biased translation performance. In this work, we propose POTSA (Parallel Optimal Transport for Speech Alignment), a new framework based on cross-lingual parallel speech pairs and Optimal Transport, designed to bridge high- and low-resource translation gaps. First, we introduce a Bias Compensation module to coarsely align initial speech representations. Second, we impose token-level OT constraints on a Q-Former using parallel pairs to establish fine-grained representation consistency. Then, we apply a layer scheduling strategy to focus OT constraints on semantically beneficial layers. Experiments on FLEURS show our method achieves SOTA performance, with +1.29 BLEU over five common languages and +2.93 BLEU on zero-shot languages, using only 10 hours of parallel speech per language.

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Xuanchen Li, Chenrui Cui, Tianrui Wang, Meng Ge, Zikang Huang, Yizhou Peng, Jin Li, Yuheng Lu, Yu Jiang, Nyima Tashi, Longbiao Wang, Jianwu Dang. 2025-11-12. POTSA: A Cross-Lingual Speech Alignment Framework for Speech-to-Text Translation. https://arxiv.org/abs/2511.09232

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