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

• URL: http://arxiv.org/abs/2503.11080v1
• Date: Fri, 14 Mar 2025 04:45:46 GMT
• Title: Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation
• Authors: Wuwei Huang, Renren Jin, Wen Zhang, Jian Luan, Bin Wang, Deyi Xiong,
• Abstract summary: 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.<n>We investigate end-to-end simultaneous speech translation in a one-to-many multilingual setting which is closer to applications in real scenarios.
• Score: 43.53370615449918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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