@inproceedings{guo-xie-2020-fa,
    title = "发音属性优化建模及其在偏误检测的应用(Speech attributes optimization modeling and application in mispronunciation detection)",
    author = "Guo, Minghao  and
      Xie, Yanlu",
    editor = "Sun, Maosong  and
      Li, Sujian  and
      Zhang, Yue  and
      Liu, Yang",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.ccl-1.7/",
    pages = "66--76",
    language = "zho",
    abstract = "近年来,发音属性常常被用于计算机辅助发音训练系统(CAPT)中。本文针对使用发音属性的一些难点,提出了一种建模细颗粒度发音属性(FSA)的方法,并在跨语言属性识别、发音偏误检测中进行测试。最终,我们得到了最优平均识别准确率约为95{\%}的属性检测器组;在两个二语测试集上的偏误检测,相比基线,基于FSA方法均获得了超过1{\%}的性能提升。此外,我们还根据发音属性的跨语言特性设置了对照实验,并在上述任务中测试和分析。"
}