Cross-Informed Domain Adversarial Training for Noise-Robust Wake-Up Word Detection

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A proper representation that can well express the characteristics of a word plays an important role in wake-up word detection (WWD). However, it may be easily corrupted due to various types of environmental noise occurred in the place where WWD typically works, causing unreliable performance. To deal with this practical issue, we propose a novel strategy called cross-informed domain adversarial training (CiDAT) for noise-robust WWD. In the method, additional paths were introduced to conventional domain adversarial training (DAT) to encourage its ability to generate domain-invariant representation. Experiments on the Aurora4 corpus verified that CiDAT significantly outperformed the baselines as well as conventional DAT.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-09
Language
English
Article Type
Article
Citation

IEEE SIGNAL PROCESSING LETTERS, v.27, pp.1769 - 1773

ISSN
1070-9908
DOI
10.1109/LSP.2020.3026947
URI
http://hdl.handle.net/10203/277117
Appears in Collection
EE-Journal Papers(저널논문)
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