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

Haihua Xu, Hang Su, Chongjia Ni, Xiong Xiao, Hao Huang, Eng Siong Chng, Haizhou Li


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.


DOI: 10.21437/Interspeech.2016-1099

Cite as

Xu, H., Su, H., Ni, C., Xiao, X., Huang, H., Chng, E.S., Li, H. (2016) Semi-Supervised and Cross-Lingual Knowledge Transfer Learnings for DNN Hybrid Acoustic Models Under Low-Resource Conditions. Proc. Interspeech 2016, 1315-1319.

Bibtex
@inproceedings{Xu+2016,
author={Haihua Xu and Hang Su and Chongjia Ni and Xiong Xiao and Hao Huang and Eng Siong Chng and Haizhou Li},
title={Semi-Supervised and Cross-Lingual Knowledge Transfer Learnings for DNN Hybrid Acoustic Models Under Low-Resource Conditions},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-1099},
url={http://dx.doi.org/10.21437/Interspeech.2016-1099},
pages={1315--1319}
}