Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information. (arXiv:2203.01826v1 [eess.AS])

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 scarcity problem, data augmentation
is commonly used for model pretraining. In this paper, we propose a phone-level
mixup, a simple yet effective data augmentation method, to improve the
performance of word-level pronunciation scoring. Specifically, given a phoneme
sequence from lexicon, the artificial augmented word sample can be generated by
randomly sampling from the …

arxiv augmentation data information phone scoring