@inproceedings{guo-etal-2023-hw,
    title = "The {HW}-{TSC}{'}s Simultaneous Speech-to-Text Translation System for {IWSLT} 2023 Evaluation",
    author = "Guo, Jiaxin  and
      Wei, Daimeng  and
      Wu, Zhanglin  and
      Li, Zongyao  and
      Rao, Zhiqiang  and
      Wang, Minghan  and
      Shang, Hengchao  and
      Chen, Xiaoyu  and
      Yu, Zhengzhe  and
      Li, Shaojun  and
      Xie, Yuhao  and
      Lei, Lizhi  and
      Yang, Hao",
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.iwslt-1.35",
    doi = "10.18653/v1/2023.iwslt-1.35",
    pages = "376--382",
    abstract = "In this paper, we present our submission to the IWSLT 2023 Simultaneous Speech-to-Text Translation competition. Our participation involves three language directions: English-German, English-Chinese, and English-Japanese. Our proposed solution is a cascaded incremental decoding system that comprises an ASR model and an MT model. The ASR model is based on the U2++ architecture and can handle both streaming and offline speech scenarios with ease. Meanwhile, the MT model adopts the Deep-Transformer architecture. To improve performance, we explore methods to generate a confident partial target text output that guides the next MT incremental decoding process. In our experiments, we demonstrate that our simultaneous strategies achieve low latency while maintaining a loss of no more than 2 BLEU points when compared to offline systems.",
}

In this paper, we present our submission to the IWSLT 2023 Simultaneous Speech-to-Text Translation competition. Our participation involves three language directions: English-German, English-Chinese, and English-Japanese. Our proposed solution is a cascaded incremental decoding system that comprises an ASR model and an MT model. The ASR model is based on the U2++ architecture and can handle both streaming and offline speech scenarios with ease. Meanwhile, the MT model adopts the Deep-Transformer architecture. To improve performance, we explore methods to generate a confident partial target text output that guides the next MT incremental decoding process. In our experiments, we demonstrate that our simultaneous strategies achieve low latency while maintaining a loss of no more than 2 BLEU points when compared to offline systems.
Guo, Jiaxin Wei, Daimeng Wu, Zhanglin Li, Zongyao Rao, Zhiqiang Wang, Minghan Shang, Hengchao Chen, Xiaoyu Yu, Zhengzhe Li, Shaojun Xie, Yuhao Lei, Lizhi Yang, Hao
The HW-TSC's Simultaneous Speech-to-Text Translation System for IWSLT 2023 Evaluation