@inproceedings{ding-etal-2025-speech,
    title = "Speech-Driven Editing System for {C}hinese {ASR} Errors",
    author = "Ding, Sji-Jie  and
      Chang, Chia-Hui  and
      Jian, Zi-Xuan",
    editor = "Chang, Kai-Wei  and
      Lu, Ke-Han  and
      Yang, Chih-Kai  and
      Tam, Zhi-Rui  and
      Chang, Wen-Yu  and
      Wang, Chung-Che",
    booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
    month = nov,
    year = "2025",
    address = "National Taiwan University, Taipei City, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard/2025.rocling-main.29/",
    pages = "277--285",
    ISBN = "979-8-89176-379-1",
    abstract = "Despite recent advances in AI, ASR systems still struggle with real-world errors from pronunciation and homophones. To solve this issue, we propose a verbal-command-based correction system that enables users to utter natural-language instructions to refine recognition outputs with minimal effort. The system consists of three modules: an input classifier, a command classifier, and a correction labeler. To support training and evaluation, we simulate ASR errors via TTS and ASR pipelines to simulate the potential errors, followed by verbal correction commands issued based on linguistic features or LLMs. Experiments show that the overall system achieves over 80{\%} correction accuracy and delivers stable performance. Compared to manual correction, this system also demonstrates highly competitive correction speed, which sufficiently indicates its feasibility for practical deployment."
}