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T. N. Sainath and C. Parada, “Convolutional neural networks for small-footprint keyword spotting,” in
2015
Earlier work this paper cites.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,”
Original
2015
Earlier work this paper cites.
B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,”
Original
2016
Earlier work this paper cites.
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin, “Large-scale evolution of image classifiers,” in
2017
Earlier work this paper cites.
R. Tang and J. Lin, “Honk: A pytorch reimplementation of convolutional neural networks for keyword spotting,”
Original
2017
Earlier work this paper cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in
2017
Earlier work this paper cites.
L. Kaiser, A. N. Gomez, and F. Chollet, “Depthwise separable convolutions for neural machine translation,”
Original
2017
Earlier work this paper cites.
R. Tang and J. Lin, “Deep residual learning for small-footprint keyword spotting,” in
2018
Earlier work this paper cites.
P. Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,”
Original
2018
Earlier work this paper cites.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in
2018
Earlier work this paper cites.

Similar

C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, “Progressive neural architecture search,” in
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2018
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T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,”
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2018
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M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le, “Mnasnet: Platform-aware neural architecture search for mobile,” 2018
2018
Cited alongside, same era.
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C.-C. Kao, M. Sun, Y. Gao, S. Vitaladevuni, and C. Wang, “Sub-band convolutional neural networks for small-footprint spoken term classification,”
Original
2019
Later among the works it cites.
M. Zeng and N. Xiao, “Effective combination of densenet and bilstm for keyword spotting,”
2019
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J. Pons and X. Serra, “Randomly weighted cnns for (music) audio classification,” in
2019
Later among the works it cites.
X. Chen, S. Yin, D. Song, P. Ouyang, L. Liu, and S. Wei, “Small-footprint keyword spotting with graph convolutional network,”
Original
2019
Later among the works it cites.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized evolution for image classifier architecture search,” in
2019
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2020

Mo, Tong, Yu, Yakun, Salameh, Mohammad et al.

Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice.

In this paper, we apply neural architecture search to search for convolutional neural network models that can help boost the performance of keyword spotting based on features extracted from acoustic signals while maintaining an acceptable memory footprint.
Specifically, we use differentiable architecture search techniques to search for operators and their connections in a predefined cell search space.
The found cells are then scaled up in both depth and width to achieve competitive performance.

T. N. Sainath and C. Parada, “Convolutional neural networks for small-footprint keyword spotting,” in
2015
Earlier work this paper cites.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,”
Original
2015
Earlier work this paper cites.
B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,”
Original
2016
Earlier work this paper cites.
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin, “Large-scale evolution of image classifiers,” in
2017
Earlier work this paper cites.
R. Tang and J. Lin, “Honk: A pytorch reimplementation of convolutional neural networks for keyword spotting,”
Original
2017
Earlier work this paper cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in
2017
Earlier work this paper cites.
L. Kaiser, A. N. Gomez, and F. Chollet, “Depthwise separable convolutions for neural machine translation,”
Original
2017
Earlier work this paper cites.
R. Tang and J. Lin, “Deep residual learning for small-footprint keyword spotting,” in
2018
Earlier work this paper cites.
P. Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,”
Original
2018
Earlier work this paper cites.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in
2018
Earlier work this paper cites.

C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, “Progressive neural architecture search,” in
2018
Cited alongside, same era.
H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,”
Original
2018
Cited alongside, same era.
T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,”
Original
2018
Cited alongside, same era.
M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le, “Mnasnet: Platform-aware neural architecture search for mobile,” 2018
2018
Cited alongside, same era.
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean, “Efficient neural architecture search via parameter sharing,”
Original

C.-C. Kao, M. Sun, Y. Gao, S. Vitaladevuni, and C. Wang, “Sub-band convolutional neural networks for small-footprint spoken term classification,”
Original
2019
Later among the works it cites.
M. Zeng and N. Xiao, “Effective combination of densenet and bilstm for keyword spotting,”
2019
Later among the works it cites.
J. Pons and X. Serra, “Randomly weighted cnns for (music) audio classification,” in
2019
Later among the works it cites.
X. Chen, S. Yin, D. Song, P. Ouyang, L. Liu, and S. Wei, “Small-footprint keyword spotting with graph convolutional network,”
Original
2019
Later among the works it cites.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized evolution for image classifier architecture search,” in
2019
Later among the works it cites.