Semi-Supervised and Cross-Lingual Knowledge Transfer Learnings for DNN Hybrid Acoustic Models Under Low-Resource Conditions

Semi-supervised and cross-lingual knowledge transfer learnings are two strategies for boosting performance of low-resource speech recognition systems. In this paper, we propose a unified knowledge transfer learning method to deal with these two learning tasks. Such a knowledge transfer learning is realized by fine-tuning of Deep Neural Network (DNN). We demonstrate its effectiveness in both monolingual based semi-supervised learning task and cross-lingual knowledge transfer learning task. We then combine these two learning strategies to obtain further performance improvement.