A study of automatic annotation of PETs with articulatory features
Xing Chun Wei, Jingping Chen, Wei Wang, Yanlu Xie, Jinsong Zhang · 2017
Compared with corrective feedback related with pronunciation score, articulatory feedback (e.g., manner and place of articulation) is more informative and instructive in learning second language (L2). Hence, our previous work directly model and detect articulatory-level mispronunciation patterns to provide readable pronunciation erroneous tendencies (PETs) feedback. However, articulatory-level labeling is much more challenging than phone-level labelling, especially facing nonnative mispronunciations, which is pronounced by using irregular articulation manner and place. Therefore, in this paper, we proposed to use log posterior of articulatory features derived from trained DNN systems to provide labeling candidates to assist annotators make final decision. Experimental results show that the consistency rate of manual labels in our system increased from 80.7% to 92.48% In addition, the time cost for annotating each sentence reduced from about 10 minutes to 3 minutes. The reported result demonstrated the efficiency of our proposed method.