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arXiv · 2503.11080

Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation

Wuwei Huang·Renren Jin·Wen Zhang·Jian Luan·Bin Wang·Deyi Xiong
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Abstract

Recent studies on end-to-end speech translation(ST) have facilitated the exploration of multilingual end-to-end ST and end-to-end simultaneous ST. In this paper, we investigate end-to-end simultaneous speech translation in a one-to-many multilingual setting which is closer to applications in real scenarios. We explore a separate decoder architecture and a unified architecture for joint synchronous training in this scenario. To further explore knowledge transfer across languages, we propose an asynchronous training strategy on the proposed unified decoder architecture. A multi-way aligned multilingual end-to-end ST dataset was curated as a benchmark testbed to evaluate our methods. Experimental results demonstrate the effectiveness of our models on the collected dataset. Our codes and data are available at: https://github.com/XiaoMi/TED-MMST.

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Wuwei Huang, Renren Jin, Wen Zhang, Jian Luan, Bin Wang, Deyi Xiong. 2025-03-14. Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation. https://arxiv.org/abs/2503.11080

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