Deep Feedforward Sequential Memory Networks Based Mispronunciation Detection for Tibetan Students' Mandarin
Zhenye Gan, Tianqin Zhao, Xinke Yu, Hongwu Yang · 2021 2nd International Conference on Information Science and Education (ICISE-IE) · 2021
Computer assisted pronunciation training system (CAPT) can detect the wrong pronunciation produced by non-native speakers and provide positive feedback. CAPT is helpful to improve the pronunciation level for L2 learners’ accurately. Tibetan students’ Mandarin is influenced by their native language pronunciation habits. So, this feature leads to their pronunciation being obviously different from that of standard mandarin. This paper used CNN model as a fundamental, and we introduced acoustic model: DFSMN and CTC. This acoustic model implemented a method of speech recognition on Tibetan students’ mandarin mispronunciation detection. In order to continue improving the detection performance, we used extended initial final (XIF) as bias primitives and design 64 bias types. Experiment results show that the proposed method in this paper can effectively detect mispronunciation and provide correct feedback, with the DA of 88.02%, FRR of 7.95% and FAR of 25.74%.