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In this paper, we explore various approaches for semi supervised learning in an end to end automatic speech recognition (ASR) framework.
2011
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G. Hinton, L. Deng, D. Yu, G. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, B. Kingsbury
2012
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A. Rousseau, P. Deléglise, and Y. Esteve, “Ted-lium: an automatic speech recognition dedicated corpus.” in
2012
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L. Deng, G. Hinton, and B. Kingsbury, “New types of deep neural network learning for speech recognition and related applications: An overview,” in
2013
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D.-H. Lee, “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in
2013
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P. Bachman, O. Alsharif, and D. Precup, “Learning with pseudo-ensembles,” in
2014
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,”
2014
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P. Motlicek, D. Imseng, B. Potard, P. N. Garner, and I. Himawan, “Exploiting foreign resources for dnn-based asr,”
2015
Cited alongside, same era.
Y. Miao, M. Gowayyed, and F. Metze, “Eesen: End-to-end speech recognition using deep rnn models and wfst-based decoding,” in
2015
Cited alongside, same era.
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: an asr corpus based on public domain audio books,” in
2015
Cited alongside, same era.
D. Povey, V. Peddinti, D. Galvez, P. Ghahremani, V. Manohar, X. Na, Y. Wang, and S. Khudanpur, “Purely sequence-trained neural networks for asr based on lattice-free mmi.” 2016
2016
Cited alongside, same era.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in
2016
Cited alongside, same era.
2017
Later among the works it cites.
2018
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V. Manohar, H. Hadian, D. Povey, and S. Khudanpur, “Semi-supervised training of acoustic models using lattice-free mmi,” in
2018
Later among the works it cites.
S. Karita, S. Watanabe, T. Iwata, A. Ogawa, and M. Delcroix, “Semi-supervised end-to-end speech recognition,” in
2018
Later among the works it cites.
T. Hori, J. Cho, and S. Watanabe, “End-to-end speech recognition with word-based rnn language models,” in
2018
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S. Kim, T. Hori, and S. Watanabe, “Joint ctc-attention based end-to-end speech recognition using multi-task learning,” in
2017
Cited alongside, same era.
A. Vyas, P. Dighe, S. Tong, and H. Bourlard, “Analyzing uncertainties in speech recognition using dropout,” in
2019
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