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For the task of speech enhancement, local learning objectives are agnostic to phonetic structures helpful for speech recognition.
“Ohio supercomputer center,” http://osc.edu/ark:/19495/f5s1ph73 , 1987
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“The Kaldi speech recognition toolkit,”
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“The second ‘CHiME’ speech separation and recognition challenge: Datasets, tasks and baselines,”
E. Vincent, J. Barker, S. Watanabe, J. Le Roux, F. Nesta, and M. Matassoni, · 2013
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A. Narayanan and D. Wang, · 2015
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K. Han, Y. Wang, D. Wang, W. S. Woods, I. Merks, and T. Zhang, · 2015
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K. Han, Y. He, D. Bagchi, E. Fosler-Lussier, and D. Wang, · 2015
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“Combining spectral feature mapping and multi-channel model-based source separation for noise-robust automatic speech recognition,”
D. Bagchi, M. I. Mandel, Z. Wang, Y. He, A. Plummer, and E. Fosler-Lussier, · 2015
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“Integration of speech enhancement and recognition using long-short term memory recurrent neural network,”
Z. Chen, S. Watanabe, H. Erdogan, and J. Hershey, · 2015
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“Speech enhancement with lstm recurrent neural networks and its application to noise-robust ASR,”
F. Weninger, H. Erdogan, S. Watanabe, E. Vincent, J. Le Roux, J. R. Hershey, and B. Schuller, · 2015
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“Image-to-image translation with conditional adversarial networks,”
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, · 2016
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T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, · 2016
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Y. Qian and P. C. Woodland, · 2016
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K. Markov and T. Matsui, · 2016
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D. Michelsanti and Z.-H. Tan, · 2017
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“Student-teacher network learning with enhanced features,”
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“A joint training framework for robust automatic speech recognition,”
Z.-Q. Wang and D. Wang, · 2016
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“The USTC-iFlytek system for CHiME-4 challenge,”
J. Du, Y.-H. Tu, L. Sun, F. Ma, H.-K. Wang, J. Pan, C. Liu, J.-D. Chen, and C.-H. Lee, · 2016
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S. Watanabe, T. Hori, J. Le Roux, and J. R. Hershey, · 2017
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“Large-scale domain adaptation via teacher-student learning,”
J. Li, M. L. Seltzer, X. Wang, R. Zhao, and Y. Gong, · 2017
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