CASIA OpenIR  > 模式识别国家重点实验室  > 语音交互
Adversarial Multilingual Training for Low-resource Speech Recognition
Yi JY(易江燕)1,2; Tao Jianhua1,2,3; Wen Zhengqi1; Bai Ye1,2
2018-04-15
Conference Name2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018)
Pages4899-4903
Conference Date15–20 April, 2018
Conference PlaceCalgary, Alberta, Canada
Abstract
This paper proposes an adversarial multilingual training to train bottleneck (BN) networks for the target language. A parallel shared-exclusive model is also proposed to train the BN network. Adversarial training is used to ensure that the shared layers can learn language-invariant features. Experiments are conducted on IARPA Babel datasets. The results show that the proposed adversarial multilingual BN model outperforms the baseline BN model by up to 8.9% relative word error rate (WER) reduction. The results also show that the proposed parallel shared-exclusive model achieves up to 1.7% relative WER reduction when compared
with the stacked share-exclusive model.
KeywordSpeech Recognition Low-resource Deep Neural Networks Bottleneck Features Adversarial Multilingual Training
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20908
Collection模式识别国家重点实验室_语音交互
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
3.CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yi JY,Tao Jianhua,Wen Zhengqi,et al. Adversarial Multilingual Training for Low-resource Speech Recognition[C],2018:4899-4903.
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