Journal
ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY
Volume 107, Issue -, Pages 842-847Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2017.03.179
Keywords
Multilingual CLDNN; LSTM; CNN; Cross-lingual
Categories
Funding
- National Natural Science Foundation of China [61231015, 61501410]
- State Administration of Press, Publication, Radio, Film and Television of the People's Republic of China [2015-53]
- Communication University of China [3132014XNG1425]
Ask authors/readers for more resources
Stand-alone and the combined model of Convolutional Neural networks (CNNs) and Long Short-Term Memory (LSTM) and Deep neural Networks (DNNs) have shown great improvements in a variety of Speech Recognition tasks. In this paper we also combined these networks but in this paper we used them for multilingual speech recognition, for the prediction and correction (PAC) architecture, in order to calculate the state probability. Our proposed model is known as PAC-MCLDNN. In this paper, we present experiment results for multilingual training on AP16-OLR task. Furthermore, cross-lingual model transfer and multitask learning for under resourced languages such as Uyghur and Vietnam are also performed which further improved the recognition results.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available