@inproceedings{hu-etal-2025-ji,
    title = "基于自监督表征蒸馏的{W}hisper低资源语音识别优化方法",
    author = "Hu, Jian  and
      Dong, Ling  and
      Wang, Wenjun  and
      Xiang, Yan  and
      Gao, Shengxiang  and
      Yu, Zhengtao",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-1.42/",
    pages = "563--573",
    abstract = "``Whisper是一种强大的多语言语音识别模型,在英语等高资源语言上表现优异,但在缅甸语等部分低资源语言的性能仍受限于预训练数据的不足。为此,本文提出了一种基于自监督表征蒸馏的Whisper低资源语音识别优化方法。通过跨模型表征蒸馏机制,实现自监督模型表征向Whisper编码器的知识迁移,提升对缅甸语等语言的表征建模能力。实验结果表明,该方法在缅甸语、柬埔寨语、乌兹别克语和旁遮普语ASR任务中有效降低了字符错误率,验证了所提方法的有效性。''"
}