Non-native acoustic modeling for mispronunciation verification based on language adversarial representation learning.

Non-native mispronunciation verification is designed to provide feedback to guide language learners to correct their pronunciation errors in their further learning and it plays an important role in the computer-aided pronunciation training (CAPT) system. Most existing approaches focus on establishing the acoustic model directly using non-native corpus thus they are suffering the data sparsity problem due to time-consuming non-native speech data collection and annotation tasks. In this work, to address this problem, we propose a pre-trained approach to utilize the speech data of two native languages (the learner's native and target languages) for non-native mispronunciation verification.