发音属性优化建模及其在偏误检测中的应用

Speech Attributes Optimization and Its Application in Mispronunciation Detection

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。该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,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表明,相比基线,基于FSA的方法均获得了超过1%的性能提升。此外,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。

In recent years,speech attributes are often used in computer-aided pronunciation training systems(CAPT).This paper proposes a method for modeling fine-grained speech attributes(FSA),and applies it in cross-language attribute recognition and mispronunciation detection.We achieve an attribute detector group with an optimal average recognition accuracy rate of 95%.As for the mispronunciation detection on the two second language test sets,FSA method achieves an improvement of more than 1%compared to the baselines.In addition,according to the cross-language characteristics of speech attribute,we also set up a comparative experiment to futher test and analyze the methods.