Meta adversarial learning improves low-resource speech recognition.

Low-resource automatic speech recognition is a challenging task. To resolve this issue, multilingual meta-learning learns a better model initialization from many source languages, allowing for rapid adaption to target languages. However, differences in data scales and learning difficulties vary greatly from one language to another. As a result, the model favors large-scale and simple source languages. Moreover, the shared semantic space of various languages is difficult to learn due to a lack of restrictions on multilingual pre-training. In this paper, we propose a meta adversarial learning approach to address this problem. The meta-learner will be guided to learn language-independent information by using an adversarial auxiliary objective of language identification, which makes the shared semantic space more compact and improves model generalization. Additionally, we optimize adversarial training using Wasserstein distance and temporal normalization, enabling more stable and simple training. Experiment results on IARPA BABEL and OpenSLR show a significant performance improvement. It also outperforms state-of-the-art results by a large margin in all target languages, and especially in few-shot settings. Finally, we demonstrate how our method is superior by using t-SNE visualization. • Multilingual meta-learning ignore the imbalance problem across source languages. • Meta adversarial learning can build a more compact semantic space and improves the generalization capability of the model. • Optimized adversarial learning will be more stable by using Wasserstein distance and temporal normalization. • Experiments on target languages verify the effectiveness of meta adversarial learning, especially in very low-resource setting. • Experiments analysis shows the principle and superiority of meta adversarial learning. [ABSTRACT FROM AUTHOR]