Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910875763343360 |
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| author | Huang, Wuwei Jin, Renren Zhang, Wen Luan, Jian Wang, Bin Xiong, Deyi |
| author_facet | Huang, Wuwei Jin, Renren Zhang, Wen Luan, Jian Wang, Bin Xiong, Deyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11080 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation Huang, Wuwei Jin, Renren Zhang, Wen Luan, Jian Wang, Bin Xiong, Deyi Computation and Language Sound Audio and Speech Processing 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. |
| title | Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2503.11080 |