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

• URL: http://arxiv.org/abs/2511.09232v1
• Date: Thu, 13 Nov 2025 01:41:51 GMT
• Title: POTSA: A Cross-Lingual Speech Alignment Framework for Low Resource Speech-to-Text Translation
• Authors: Xuanchen Li, Chenrui Cui, Tianrui Wang, Meng Ge, Zikang Huang, Jin Li, Yizhou Peng, Longbiao Wang, Jianwu Dang, Nyima Tashi,
• Abstract summary: We propose a new framework based on cross-lingual parallel speech pairs and Optimal Transport (OT) to bridge high- and low-resource translation gaps.<n>Our method achieves SOTA performance, with +0.93 BLEU on average over five common languages and +5.05 BLEU on zero-shot languages.
• Score: 47.51298472124902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech Large Language Models (SpeechLLMs) have achieved breakthroughs in multilingual speech-to-text translation (S2TT). However, existing approaches often overlook semantic commonalities across source languages, leading to biased translation performance. In this work, we propose \textbf{POTSA} (Parallel Optimal Transport for Speech Alignment), a new framework based on cross-lingual parallel speech pairs and Optimal Transport (OT), designed to bridge high- and low-resource translation gaps. First, we introduce a Bias Compensation module to coarsely align initial speech representations across languages. Second, we impose token-level OT constraints on a Q-Former using parallel speech pairs to establish fine-grained consistency of representations. Then, we apply a layer scheduling strategy to focus OT constraints on the most semantically beneficial layers. Experiments on the FLEURS dataset show that our method achieves SOTA performance, with +0.93 BLEU on average over five common languages and +5.05 BLEU on zero-shot languages, using only 10 hours of parallel speech per source language.

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