@inproceedings{tan-etal-2025-naist,
    title = "{NAIST} Simultaneous Speech Translation System for {IWSLT} 2025",
    author = "Tan, Haotian  and
      Faradishi Widiaputri, Ruhiyah  and
      Meyer Saragih, Jan  and
      Ko, Yuka  and
      Sudoh, Katsuhito  and
      Nakamura, Satoshi  and
      Sakti, Sakriani",
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Anastasopoulos, Antonis",
    booktitle = "Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.iwslt-1.39/",
    pages = "369--378",
    ISBN = "979-8-89176-272-5",
    abstract = "This paper describes the NAIST submission to the English-to-German, Japanese, Chinese Simultaneous Speech-to-Text track at IWSLT 2025. Last year, our system was based on an end-to-end speech-to-text translation model that combined HuBERT and mBART. This year, the system consists of a Whisper encoder, the DeCo compressive projector, and the Qwen large language model. The simultaneous translation (SimulST) system is implemented by applying a local agreement policy to an offline-trained translation model. For the streaming translation (StreamST) system, we integrate an online version of the SHAS segmenter into our SimulST architecture. Our results demonstrate that adopting LLMs as the backbone architecture for speech translation tasks yields strong translation performance. Additionally, leveraging robust segmentation capability of SHAS for StreamST achieves good quality-latency trade-off when processing unbounded audio streams."
}