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This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models.
R. Lippmann, E. Martin, and D. Paul, “Multi-style training for robust isolated-word speech recognition,” in
1987
Earlier work this paper cites.
H.-G. Hirsch and D. Pearce, “The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions,” in
2000
Earlier work this paper cites.
D. Pearce, “Aurora working group: Dsr front end lvcsr evaluation au/384/02,” 2002
2002
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz
2011
Earlier work this paper cites.
G. E. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition,”
2012
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath
2012
Earlier work this paper cites.
O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, and G. Penn, “Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition,” in
2012
Earlier work this paper cites.
T. N. Sainath, A.-r. Mohamed, B. Kingsbury, and B. Ramabhadran, “Deep convolutional neural networks for lvcsr,” in
2013
Earlier work this paper cites.
A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in
2013
Earlier work this paper cites.
K. Kinoshita, M. Delcroix, T. Yoshioka, T. Nakatani, A. Sehr, W. Kellermann, and R. Maas, “The reverb challenge: A common evaluation framework for dereverberation and recognition of reverberant speech,” in
2013
Earlier work this paper cites.
D. Yu, K. Yao, H. Su, G. Li, and F. Seide, “KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition,”
2013
Cited alongside, same era.
S. J. Rennie, V. Goel, and S. Thomas, “Deep order statistic networks,” in
2014
Cited alongside, same era.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,”
2014
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
2014
Cited alongside, same era.
T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, “Audio augmentation for speech recognition,” in
2015
Cited alongside, same era.
A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,”
2016
Later among the works it cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard
2016
Later among the works it cites.
J. Li, M. L. Seltzer, X. Wang, R. Zhao, and Y. Gong, “Large-scale domain adaptation via teacher-student learning,”
2017
Later among the works it cites.
S. Sun, B. Zhang, L. Xie, and Y. Zhang, “An unsupervised deep domain adaptation approach for robust speech recognition,”
2017
Later among the works it cites.
2017
Later among the works it cites.
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2015
Cited alongside, same era.
2015
Cited alongside, same era.
Y. Qian, M. Bi, T. Tan, and K. Yu, “Very deep convolutional neural networks for noise robust speech recognition,”
2016
Cited alongside, same era.
Y. Shinohara, “Adversarial multi-task learning of deep neural networks for robust speech recognition.” 2016
2016
Cited alongside, same era.
X. Xiao, C. Xu, Z. Zhang, S. Zhao, S. Sun, S. Watanabe, L. Wang, L. Xie, D. L. Jones, E. S. Chng
Cited in the paper.
S. Watanabe, T. Hori, J. Le Roux, and J. R. Hershey, “Student-teacher network learning with enhanced features,” in
2017
Later among the works it cites.
2018
Closest in time.
N. Carlini and D. Wagner, “Audio adversarial examples: Targeted attacks on speech-to-text,”
2018
Closest in time.
R. Jia and P. Liang, “Adversarial examples for evaluating reading comprehension systems,” in
2031
Closest in time.