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
Institute of Electrical and Electronics Engineers
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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