Fetching the paper…
Reading the bibliography…
Keyword Spotting (KWS) enables speech-based user interaction on smart devices.
“An application of recurrent neural networks to discriminative keyword spotting,”
S. Fernández, A. Graves, and J. Schmidhuber, · 2007
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
A. Krizhevsky, I. Sutskever, and G. E. Hinton, · 2012
Earlier work this paper cites.
“Small-footprint keyword spotting using deep neural networks,”
G. Chen, C. Parada, and G. Heigold, · 2014
Earlier work this paper cites.
“1.1 computing’s energy problem (and what we can do about it),”
M. Horowitz, · 2014
Earlier work this paper cites.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
S. Ioffe and C. Szegedy, · 2015
Earlier work this paper cites.
“Efficient object localization using convolutional networks,”
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler, · 2015
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
J. Ba D. P. Kingma, · 2015
Earlier work this paper cites.
“Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks,”
Y. Chen, J. Emer, and V. Sze, · 2016
Earlier work this paper cites.
“Hello edge: Keyword spotting on microcontrollers,”
Y. Zhang, N. Suda, L. Lai, and V. Chandra, · 2017
Cited alongside, same era.
“Xception: Deep learning with depthwise separable convolutions,”
F. Chollet, · 2017
Cited alongside, same era.
“Efficient processing of deep neural networks: A tutorial and survey,”
V. Sze, Y. Chen, T. Yang, and J. S. Emer, · 2017
Cited alongside, same era.
“Direct modeling of raw audio with dnns for wake word detection,”
K. Kumatani, S. Panchapagesan, M. Wu, M. Kim, N. Strom, G. Tiwari, and A. Mandai, · 2017
Cited alongside, same era.
“Mobilenets: Efficient convolutional neural networks for mobile vision applications,”
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, · 2017
Cited alongside, same era.
“Depthwise separable convolutions for neural machine translation,”
L. Kaiser, A. N. Gomez, and F. Chollet, · 2018
Later among the works it cites.
“Interpretable convolutional filters with sincnet,”
M. Ravanelli and Y. Bengio, · 2018
Later among the works it cites.
“Learning filterbanks from raw speech for phone recognition,”
N. Zeghidour, N. Usunier, I. Kokkinos, T. Schaiz, G. Synnaeve, and E. Dupoux, · 2018
Later among the works it cites.
“End-to-end speech recognition from the raw waveform,”
N. Zeghidour, N. Usunier, G. Synnaeve, R. Collobert, and E. Dupoux, · 2018
Later among the works it cites.
“Studying the effects of feature extraction settings on the accuracy and memory requirements of neural networks for keyword spotting,”
M. Shahnawaz, E. Plebani, I. Guaneri, D. Pau, and M. Marcon, · 2018
Later among the works it cites.
“Speech commands: A dataset for limited-vocabulary speech recognition,”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Deep residual learning for small-footprint keyword spotting,”
R. Tang and J. Lin, · 2018
Cited alongside, same era.
“Speaker recognition from raw waveform with sincnet,”
M. Ravanelli and Y. Bengio, · 2018
Cited alongside, same era.
P. Warden, · 2018
Later among the works it cites.
“Temporal Convolution for Real-Time Keyword Spotting on Mobile Devices,”
S. Choi, S. Seo, B. Shin, H. Byun, M. Kersner, B. Kim, D. Kim, and S. Ha, · 2019
Closest in time.