基于发音特征的发音偏误趋势检测研究

发布时间：2018-03-06 21:14

本文选题：发音特征　切入点：发音偏误趋势　出处：《北京大学学报(自然科学版)》2017年02期 　论文类型：期刊论文

【摘要】：为了提升计算机辅助发音训练(CAPT)系统中发音偏误趋势(PET)的检测效果,确保反馈信息的准确性与有效性,提出一种基于对数似然比的发音特征方法。该方法将多个基于深度神经网络的发音特征提取器用于生成帧级别的对数似然比,然后将对数似然比组成的发音特征用于PET的检测,为学习者提供发音位置和发音方法的正音信息。实验结果表明,发音特征对PET的检测效果优于常用声学特征(MFCC,PLP和f Bank),当发音特征与MFCC特征相结合时,可以进一步提升性能,达到错误接受率为5.0%,错误拒绝率为30.8%,诊断正确率为89.8%的检测效果。
[Abstract]:In order to improve the detection effect of pronunciation bias trend in CAPTT system, and ensure the accuracy and effectiveness of feedback information, A method of pronunciation feature based on logarithmic likelihood ratio (LLR) is proposed, in which several speech feature extractors based on depth neural network are used to generate logarithmic likelihood ratio at frame level, and then the pronunciation feature composed of logarithmic likelihood ratio is used for PET detection. The experimental results show that pronunciation features are more effective than common acoustic features, such as PET and f BankP, and can further improve the performance when the pronunciation features are combined with MFCC features. The error acceptance rate is 5.0%, the error rejection rate is 30.8%, and the diagnostic accuracy rate is 89.8%.
【作者单位】：北京语言大学信息科学学院;
【基金】：北京语言大学梧桐创新平台项目(16PT05)和北京语言大学研究生创新基金项目(16YCX160)资助
【分类号】：H01;TP391.7

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