@inproceedings{faradishi-widiaputri-etal-2025-naist,
    title = "{NAIST} Offline Speech Translation System for {IWSLT} 2025",
    author = "Faradishi Widiaputri, Ruhiyah  and
      Tan, Haotian  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-1.38/",
    pages = "360--368",
    ISBN = "979-8-89176-272-5",
    abstract = "This paper presents NAIST{'}s submission to the offline speech translation task of the IWSLT 2025 evaluation campaign, focusing on English-to-German and English-to-Chinese translation. We implemented both cascade and end-to-end frameworks using various components. For the cascade approach, we used Whisper and SALMONN as automatic speech recognition systems, each paired with Qwen2.5 large language model (LLM) for translation. In the end-to-end setting, we used SALMONN as speech translation and also built a custom model combining the Whisper encoder, DeCo projector, and Qwen2.5 LLM. To further leverage the large language model capabilities, we experimented with different prompting strategies. Additionally, since long speech inputs are segmented for processing, we applied hypothesis combination techniques to generate the final translation output. Our results show that combining Whisper and LLMs can yield strong translation performance, even without further fine-tuning in the cascade setup. Moreover, our proposed end-to-end architecture achieved competitive results, despite being trained on significantly less data compared to SALMONN. Finally, we decided to use both SALMONN as an end-to-end speech translation model and our proposed end-to-end model for our IWSLT 2025 submission for both language pairs."
}