We are developing a CALL system to train English pronunciation for Japanese native speakers. However, the precision of the error detection was not very high because the threshold for the detection was not optimum. To improve the detection accuracy, we propose a new method to optimize the thresholds of error detection. The proposed method makes several clusters of the pronunciation error rules, and the thresholds are determined for each cluster. An experiment was carried out to investigate the performance of the proposed method. As a result, about 90% of detection rate was obtained, which is a remarkable improvement from the conventional method.
Cite as: Ito, A., Lim, Y.-L., Suzuki, M., Makino, S. (2005) Pronunciation error detection method based on error rule clustering using a decision tree. Proc. Interspeech 2005, 173-176, doi: 10.21437/Interspeech.2005-96
@inproceedings{ito05_interspeech,
author={Akinori Ito and Yen-Ling Lim and Motoyuki Suzuki and Shozo Makino},
title={{Pronunciation error detection method based on error rule clustering using a decision tree}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={173--176},
doi={10.21437/Interspeech.2005-96}
}