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Despite showing state-of-the-art performance, deep learning for speech recognition remains challenging to deploy in on-device edge scenarios such as mobile and other consumer devices.
T. N. Sainath and C. Parada, “Convolutional neural networks for small-footprint keyword spotting,” in Sixteenth Annual Conference of the International Speech Communication Association , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
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2017
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2017
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2017
Cited alongside, same era.
P. Warden, “Launching the speech commands dataset,” in Google Research Blog , 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Closest in time.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4510–4520
2018
Closest in time.
2018
Closest in time.
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