@inproceedings{zhou-etal-2023-submission,
    title = "Submission of {USTC}{'}s System for the {IWSLT} 2023 - Offline Speech Translation Track",
    author = "Zhou, Xinyuan  and
      Cui, Jianwei  and
      Ye, Zhongyi  and
      Wang, Yichi  and
      Xu, Luzhen  and
      Zhang, Hanyi  and
      Zhang, Weitai  and
      Dai, Lirong",
    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-1.15",
    doi = "10.18653/v1/2023.iwslt-1.15",
    pages = "194--201",
    abstract = "This paper describes the submissions of the research group USTC-NELSLIP to the 2023 IWSLT Offline Speech Translation competition, which involves translating spoken English into written Chinese. We utilize both cascaded models and end-to-end models for this task. To improve the performance of the cascaded models, we introduce Whisper to reduce errors in the intermediate source language text, achieving a significant improvement in ASR recognition performance. For end-to-end models, we propose Stacked Acoustic-and-Textual En- coding extension (SATE-ex), which feeds the output of the acoustic decoder into the textual decoder for information fusion and to prevent error propagation. Additionally, we improve the performance of the end-to-end system in translating speech by combining the SATE-ex model with the encoder-decoder model through ensembling.",
}

This paper describes the submissions of the research group USTC-NELSLIP to the 2023 IWSLT Offline Speech Translation competition, which involves translating spoken English into written Chinese. We utilize both cascaded models and end-to-end models for this task. To improve the performance of the cascaded models, we introduce Whisper to reduce errors in the intermediate source language text, achieving a significant improvement in ASR recognition performance. For end-to-end models, we propose Stacked Acoustic-and-Textual En- coding extension (SATE-ex), which feeds the output of the acoustic decoder into the textual decoder for information fusion and to prevent error propagation. Additionally, we improve the performance of the end-to-end system in translating speech by combining the SATE-ex model with the encoder-decoder model through ensembling.
Zhou, Xinyuan Cui, Jianwei Ye, Zhongyi Wang, Yichi Xu, Luzhen Zhang, Hanyi Zhang, Weitai Dai, Lirong
Submission of USTC's System for the IWSLT 2023 - Offline Speech Translation Track