@inproceedings{huang-etal-2023-xiaomi,
    title = "The Xiaomi {AI} Lab{'}s Speech Translation Systems for {IWSLT} 2023 Offline Task, Simultaneous Task and Speech-to-Speech Task",
    author = "Huang, Wuwei  and
      Liu, Mengge  and
      Li, Xiang  and
      Tian, Yanzhi  and
      Yang, Fengyu  and
      Zhang, Wen  and
      Luan, Jian  and
      Wang, Bin  and
      Guo, Yuhang  and
      Su, Jinsong",
    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-1.39",
    doi = "10.18653/v1/2023.iwslt-1.39",
    pages = "411--419",
    abstract = "This system description paper introduces the systems submitted by Xiaomi AI Lab to the three tracks of the IWSLT 2023 Evaluation Campaign, namely the offline speech translation (Offline-ST) track, the offline speech-to-speech translation (Offline-S2ST) track, and the simultaneous speech translation (Simul-ST) track. All our submissions for these three tracks only involve the English-Chinese language direction. Our English-Chinese speech translation systems are constructed using large-scale pre-trained models as the foundation. Specifically, we fine-tune these models{'} corresponding components for various downstream speech translation tasks. Moreover, we implement several popular techniques, such as data filtering, data augmentation, speech segmentation, and model ensemble, to improve the system{'}s overall performance. Extensive experiments show that our systems achieve a significant improvement over the strong baseline systems in terms of the automatic evaluation metric.",
}

This system description paper introduces the systems submitted by Xiaomi AI Lab to the three tracks of the IWSLT 2023 Evaluation Campaign, namely the offline speech translation (Offline-ST) track, the offline speech-to-speech translation (Offline-S2ST) track, and the simultaneous speech translation (Simul-ST) track. All our submissions for these three tracks only involve the English-Chinese language direction. Our English-Chinese speech translation systems are constructed using large-scale pre-trained models as the foundation. Specifically, we fine-tune these models{'} corresponding components for various downstream speech translation tasks. Moreover, we implement several popular techniques, such as data filtering, data augmentation, speech segmentation, and model ensemble, to improve the system{'}s overall performance. Extensive experiments show that our systems achieve a significant improvement over the strong baseline systems in terms of the automatic evaluation metric.
Huang, Wuwei Liu, Mengge Li, Xiang Tian, Yanzhi Yang, Fengyu Zhang, Wen Luan, Jian Wang, Bin Guo, Yuhang Su, Jinsong
The Xiaomi AI Lab's Speech Translation Systems for IWSLT 2023 Offline Task, Simultaneous Task and Speech-to-Speech Task