Interlayer Selective Attention Network for Robust Personalized Wake-up Word Detection

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Previous research methods on wake-up word detection (WWD) have been proposed with focus on finding a decent word representation that can well express the characteristics of a word. However, there are various obstacles such as noise and reverberation which make it difficult in real-world environments where WWD works. To tackle this, we propose a novel architecture called interlayer selective attention network (ISAN) which generates more robust word representation by introducing the concept of selective attention. Experiments in real-world scenarios demonstrated that the proposed ISAN outperformed several baseline methods as well as other attention methods. In addition, the effectiveness of ISAN was analyzed with visualizations.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-01
Language
English
Article Type
Article
Citation

IEEE SIGNAL PROCESSING LETTERS, v.27, no.1, pp.126 - 130

ISSN
1070-9908
DOI
10.1109/LSP.2019.2959902
URI
http://hdl.handle.net/10203/276849
Appears in Collection
EE-Journal Papers(저널논문)
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