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Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices.
“Continuous hidden markov modeling for speaker-independent word spotting,”
J Robin Rohlicek, William Russell, Salim Roukos, and Herbert Gish, · 1989
Earlier work this paper cites.
“A hidden markov model based keyword recognition system,”
Richard C Rose and Douglas B Paul, · 1990
Earlier work this paper cites.
“Improvements and applications for key word recognition using hidden markov modeling techniques,”
JG Wilpon, LG Miller, and P Modi, · 1991
Earlier work this paper cites.
“Iterative posterior-based keyword spotting without filler models,”
Marius-Calin Silaghi and Hervé Bourlard, · 1999
Earlier work this paper cites.
“Spotting subsequences matching an hmm using the average observation probability criteria with application to keyword spotting,”
Marius-Calin Silaghi, · 2005
Earlier work this paper cites.
“Rapid and accurate spoken term detection,”
David RH Miller, Michael Kleber, Chia-Lin Kao, Owen Kimball, Thomas Colthurst, Stephen A Lowe, Richard M Schwartz, and Herbert Gish, · 2007
Earlier work this paper cites.
“Vocabulary independent spoken term detection,”
Jonathan Mamou, Bhuvana Ramabhadran, and Olivier Siohan, · 2007
Earlier work this paper cites.
“An application of recurrent neural networks to discriminative keyword spotting,”
Santiago Fernández, Alex Graves, and Jürgen Schmidhuber, · 2007
Earlier work this paper cites.
“Spoken term detection for turkish broadcast news,”
Siddika Parlak and Murat Saraclar, · 2008
Earlier work this paper cites.
““your word is my command”: Google search by voice: A case study,”
Johan Schalkwyk, Doug Beeferman, Françoise Beaufays, Bill Byrne, Ciprian Chelba, Mike Cohen, Maryam Kamvar, and Brian Strope, · 2010
Cited alongside, same era.
“Keyword spotting exploiting long short-term memory,”
Martin Woellmer, Bjoern Schuller, and Gerhard Rigoll, · 2013
Cited alongside, same era.
“Speech recognition and keyword spotting for low-resource languages: Babel project research at cued,”
Mark JF Gales, Kate M Knill, Anton Ragni, and Shakti P Rath, · 2014
Cited alongside, same era.
“Small-footprint keyword spotting using deep neural networks,”
Guoguo Chen, Carolina Parada, and Georg Heigold, · 2014
Cited alongside, same era.
“Online word-spotting in continuous speech with recurrent neural networks,”
Pallavi Baljekar, Jill Fain Lehman, and Rita Singh, · 2014
Cited alongside, same era.
“Convolutional neural networks for small-footprint keyword spotting,”
“Hello edge: Keyword spotting on microcontrollers,”
Yundong Zhang, Naveen Suda, Liangzhen Lai, and Vikas Chandra, · 2017
Later among the works it cites.
“Streaming small-footprint keyword spotting using sequence-to-sequence models,”
Yanzhang He, Rohit Prabhavalkar, Kanishka Rao, Wei Li, Anton Bakhtin, and Ian McGraw, · 2017
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“Semi-supervised classification with graph convolutional networks,”
Thomas N. Kipf and Max Welling, · 2017
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“Neural message passing for quantum chemistry,”
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl, · 2017
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“Deep residual learning for small-footprint keyword spotting,”
Raphael Tang and Jimmy Lin, · 2018
Later among the works it cites.
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Tara N Sainath and Carolina Parada, · 2015
Cited alongside, same era.
“Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting,”
Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Gengshen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, and Shiv Vitaladevuni, · 2016
Cited alongside, same era.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Cited alongside, same era.
Pete Warden, · 2018
Later among the works it cites.
“Non-local neural networks,”
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He, · 2018
Later among the works it cites.
“Dual attention network for scene segmentation,”
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu, · 2019
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