@inproceedings{park-etal-2025-evaluating-automatic,
    title = "Evaluating Automatic Speech Recognition Systems for {K}orean Meteorological Experts",
    author = "Park, ChaeHun  and
      Cho, Hojun  and
      Choo, Jaegul",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.561/",
    doi = "10.18653/v1/2025.findings-emnlp.561",
    pages = "10619--10627",
    ISBN = "979-8-89176-335-7",
    abstract = "Automatic speech recognition systems often fail on specialized vocabulary in tasks such as weather forecasting. To address this, we introduce an evaluation dataset of Korean weather queries. The dataset was recorded by diverse native speakers following pronunciation guidelines from domain experts and underwent rigorous verification. Benchmarking both open-source models and a commercial API reveals high error rates on meteorological terms. We also explore a lightweight text-to-speech-based data augmentation strategy, yielding substantial error reduction for domain-specific vocabulary and notable improvement in overall recognition accuracy. Our dataset is available at https://huggingface.co/datasets/ddehun/korean-weather-asr."
}