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S. Choi, S. Seo, B. Shin, H. Byun, M. Kersner, B. Kim, D. Kim, S. Ha, “Temporal Convolution for Real-time Keyword Spotting on Mobile Devices,”
Original
1904
Earlier work this paper cites.
D. Park, W. Chan, Y. Zhang, C. Chiu, B. Zoph, E. Cubuk and Q. Le, “SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition,” in
Original
1904
Earlier work this paper cites.
S. B. Davis and P. Mermelstein, “Comparison of parametric representation for monosyllabic word recognition in continuously spoken sentences,”
1980
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,”
1998
Earlier work this paper cites.
F. A. Gers and J. Schmidhuber, “LSTM recurrent networks learn simple context-free and context-sensitive languages,”
2001
Earlier work this paper cites.
S. Majumdar, B. Ginsburg, “MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition,”
Original
2004
Earlier work this paper cites.
Y. Gong, L. Liu, M. Yang, and L. D. Bourdev, “Compressing deep convolutional networks using vector quantization,”
Original
2014
Earlier work this paper cites.

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2020

Rybakov, Oleg, Kononenko, Natasha, Subrahmanya, Niranjan et al.

In this work we explore the latency and accuracy of keyword spotting (KWS) models in streaming and non-streaming modes on mobile phones.

NN model conversion from non-streaming mode (model receives the whole input sequence and then returns the classification result) to streaming mode (model receives portion of the input sequence and classifies it incrementally) may require manual model rewriting.
We address this by designing a Tensorflow/Keras based library which allows automatic conversion of non-streaming models to streaming ones with minimum effort.
With this library we benchmark multiple KWS models in both streaming and non-streaming modes on mobile phones and demonstrate different tradeoffs between latency and accuracy.

S. Choi, S. Seo, B. Shin, H. Byun, M. Kersner, B. Kim, D. Kim, S. Ha, “Temporal Convolution for Real-time Keyword Spotting on Mobile Devices,”
Original
1904
Earlier work this paper cites.
D. Park, W. Chan, Y. Zhang, C. Chiu, B. Zoph, E. Cubuk and Q. Le, “SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition,” in
Original
1904
Earlier work this paper cites.
S. B. Davis and P. Mermelstein, “Comparison of parametric representation for monosyllabic word recognition in continuously spoken sentences,”
1980
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,”
1998
Earlier work this paper cites.
F. A. Gers and J. Schmidhuber, “LSTM recurrent networks learn simple context-free and context-sensitive languages,”
2001
Earlier work this paper cites.
S. Majumdar, B. Ginsburg, “MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition,”
Original
2004
Earlier work this paper cites.
Y. Gong, L. Liu, M. Yang, and L. D. Bourdev, “Compressing deep convolutional networks using vector quantization,”
Original
2014
Earlier work this paper cites.

G. Chen, C. Parada, and G. Heigold, “Small-footprint keyword spotting using deep neural networks,” in
2014
Cited alongside, same era.
K. Cho, B. van Merrienboer, Ç. Gülçehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” in
Original
2014
Cited alongside, same era.
T. N. Sainath and C. Parada, “Convolutional neural networks for small-footprint keyword spotting,” in
2015
Cited alongside, same era.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in
Original
2015
Cited alongside, same era.
M. Sun, A. Raju, G. Tucker, S. Panchapagesan, G. Fu, A. Mandal, S. Matsoukas, N. Strom, and S. Vitaladevuni, “Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting,” in

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in
2017
Later among the works it cites.
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, “Quantization and training of neural networks for efficient integer-arithmetic-only inference,” 06 2018, pp. 2704–2713
2018
Later among the works it cites.
Y. He, T. Sainath et al, “Streaming End-to-end Speech Recognition For Mobile Devices,”
Original
2018
Later among the works it cites.
P. Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,”
Original
2018
Later among the works it cites.
D. C. de Andrade, S. Leo, M. L. D. S. Viana, and C. Bernkopf, “A neural attention model for speech command recognition,”