@inproceedings{he-etal-2025-meralion,
    title = "{MER}a{L}i{ON}-{A}udio{LLM}: Advancing Speech and Language Understanding for {S}ingapore",
    author = "He, Yingxu  and
      Liu, Zhuohan  and
      Lin, Geyu  and
      Sun, Shuo  and
      Wang, Bin  and
      Zhang, Wenyu  and
      Zou, Xunlong  and
      Chen, Nancy F.  and
      Aw, AiTi",
    editor = "Mishra, Pushkar  and
      Muresan, Smaranda  and
      Yu, Tao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.3/",
    pages = "22--30",
    ISBN = "979-8-89176-253-4",
    abstract = "We introduce MERaLiON-AudioLLM, the first general-purpose audio-based large language model designed for multitask learning, with a particular focus on Singlish understanding. Trained on 62 million multimodal instruction samples comprising a total of 260k hours of audio, it exhibits strong generalization across a diverse set of tasks, including{---}but not limited to{---}automatic speech recognition, spoken question answering, speech translation, and paralinguistic analysis. Our results show significant improvements in local speech recognition and task-specific understanding, making MERaLiON-AudioLLM a leading solution for region-specific AI applications. An interactive demo has been developed to enable user-friendly interactions, supported by a backend with customized caching and load-balancing mechanisms. We benchmark the model across a broad range of multilingual and multitask scenarios, where it demonstrates competitive performance compared to other open-source models. The demo page, model weights and videos are publically accessible."
}