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Improving the performance of on-device audio classification models remains a challenge given the computational limits of the mobile environment.
“Learning precise timing with LSTM recurrent networks,”
Felix A. Gers, Nicol N. Schraudolph, and Jürgen Schmidhuber, · 2002
Earlier work this paper cites.
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Geoffrey Hinton, Oriol Vinyals, and Jeff Dean, · 2015
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“Hierarchical attention networks for document classification,”
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy, · 2016
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“Knowledge distillation for small-footprint highway networks,”
Liang Lu, Michelle Guo, and Steve Renals, · 2017
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“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
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“Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer,”
Sergey Zagoruyko and Nikos Komodakis, · 2017
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“Audio set: An ontology and human-labeled dataset for audio events,”
Jort F. Gemmeke, Daniel P. W. Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R. Channing Moore, Manoj Plakal, and Marvin Ritter, · 2017
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“Convolutional recurrent neural networks for small-footprint keyword spotting,”
Sercan Ömer Arik, Markus Kliegl, Rewon Child, Joel Hestness, Andrew Gibiansky, Christopher Fougner, Ryan Prenger, and Adam Coates, · 2017
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“Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition,”
P. Warden, · 2018
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“A neural attention model for speech command recognition,”
D. Coimbra de Andrade, S. Leo, M. Loesener Da Silva Viana, and C. Bernkopf, · 2018
Cited alongside, same era.
“Sequence-level knowledge distillation for model compression of attention-based sequence-to-sequence speech recognition,”
Raden Mu’az Mun’im, Nakamasa Inoue, and Koichi Shinoda, · 2019
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“Bert: Pre-training of deep bidirectional transformers for language understanding,”
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, · 2019
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“Temporal convolution for real-time keyword spotting on mobile devices,”
Seungwoo Choi, Seokjun Seo, Beomjun Shin, Hyeongmin Byun, Martin Kersner, Beomsu Kim, Dongyoung Kim, and Sungjoo Ha, · 2019
Cited alongside, same era.
“FSD50K: an open dataset of human-labeled sound events,”
Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, and Xavier Serra, · 2020
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“Distilling the knowledge of BERT for sequence-to-sequence ASR,”
Hayato Futami, Hirofumi Inaguma, Sei Ueno, Masato Mimura, Shinsuke Sakai, and Tatsuya Kawahara, · 2020
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“Intra-utterance similarity preserving knowledge distillation for audio tagging,”
Chun-Chieh Chang, Chieh-Chi Kao, Ming Sun, and Chao Wang, · 2020
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“Unsupervised cross-lingual representation learning for speech recognition,”
Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, and Michael Auli, · 2020
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“Quantifying attention flow in transformers,”
Samira Abnar and Willem H. Zuidema, · 2020
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“Specaugment: A simple data augmentation method for automatic speech recognition,”
Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le, · 2019
Cited alongside, same era.
“Streaming keyword spotting on mobile devices,”
Oleg Rybakov, Natasha Kononenko, Niranjan Subrahmanya, Mirkó Visontai, and Stella Laurenzo, · 2020
Cited alongside, same era.
“Keyword Transformer: A Self-Attention Model for Keyword Spotting,”
Axel Berg, Mark O’Connor, and Miguel Tairum Cruz, · 2021
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Rupak Vignesh Swaminathan, Brian King, Grant P. Strimel, Jasha Droppo, and Athanasios Mouchtaris, · 2021
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