@inproceedings{1f6ca5cb56b9496ebe26c055ea647150,
title = "Reduced Model Size Deep Convolutional Neural Networks for Small-Footprint Keyword Spotting",
abstract = "This paper discussed the application of Densely Connected Convolutional Networks (DenseNet), group convolution, and squeeze-and-excitation Networks (SENet) in keyword spotting tasks. We validated the network using the Google Speech Commands Dataset. Our proposed network has better accuracy than other networks even with less number of parameters and floating-point operations (FLOPs). In addition, we varied the depth and width of the network to build a compact variant network. It also outperforms other compact variants.",
keywords = "DenseNet, SENet, deep learning, group convolution, keyword spotting",
author = "Tsai, {Tsung Han} and Lin, {Xin Hui}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 28th IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2021 ; Conference date: 28-11-2021 Through 01-12-2021",
year = "2021",
doi = "10.1109/ICECS53924.2021.9665618",
language = "???core.languages.en_GB???",
series = "2021 28th IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 28th IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2021 - Proceedings",
}