26 citations · 31 across the 2 of their papers we have counts for
2 papers
eess.AS2022★ 5 cited
Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information
Kaiqi Fu, Shaojun Gao, Kai Wang +3
Deep learning-based pronunciation scoring models highly rely on the availability of the annotated non-native data, which is costly and has scalability issues. To deal with the data…
cs.CL2021★ 26 cited
A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques
Kaiqi Fu, Jones Lin, Dengfeng Ke +3
Recently, end-to-end mispronunciation detection and diagnosis (MD&D) systems has become a popular alternative to greatly simplify the model-building process of conventional hybrid…