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J. S. Bridle, “Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition,” in
1990
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner
1998
Earlier work this paper cites.
D. Can and M. Saraclar, “Lattice indexing for spoken term detection,”
2011
Earlier work this paper cites.
P. Motlicek, F. Valente, and I. Szoke, “Improving acoustic based keyword spotting using lvcsr lattices,” in
2012
Earlier work this paper cites.
G. Chen, C. Parada, and G. Heigold, “Small-footprint keyword spotting using deep neural networks,” in
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
Original
2014
Earlier work this paper cites.
T. N. Sainath and C. Parada, “Convolutional neural networks for small-footprint keyword spotting,” in
2015
Earlier work this paper cites.

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2020

Parnami, Archit, Lee, Minwoo

Recognizing a particular command or a keyword, keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home.

In order to recognize a set of keywords, most of the recent deep learning based approaches use a neural network trained with a large number of samples to identify certain pre-defined keywords.
This restricts the system from recognizing new, user-defined keywords.
Therefore, we first formulate this problem as a few-shot keyword spotting and approach it using metric learning.

J. S. Bridle, “Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition,” in
1990
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner
1998
Earlier work this paper cites.
D. Can and M. Saraclar, “Lattice indexing for spoken term detection,”
2011
Earlier work this paper cites.
P. Motlicek, F. Valente, and I. Szoke, “Improving acoustic based keyword spotting using lvcsr lattices,” in
2012
Earlier work this paper cites.
G. Chen, C. Parada, and G. Heigold, “Small-footprint keyword spotting using deep neural networks,” in
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
Original
2014
Earlier work this paper cites.
T. N. Sainath and C. Parada, “Convolutional neural networks for small-footprint keyword spotting,” in
2015
Earlier work this paper cites.

G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in
2015
Cited alongside, same era.
O. Vinyals, C. Blundell, T. P. Lillicrap, K. Kavukcuoglu, and D. Wierstra, “Matching networks for one shot learning,” in
2016
Cited alongside, same era.
Y. Zhang, N. Suda, L. Lai, and V. Chandra, “Hello edge: Keyword spotting on microcontrollers,”
Original
2017
Cited alongside, same era.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in
2017
Cited alongside, same era.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” in
2017
Cited alongside, same era.