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In this paper, we conduct data selection analysis in building an English-Mandarin code-switching (CS) speech recognition (CSSR) system, which is aimed for a real CSSR contest in China.
N. Itoh, T. N. Sainath, D. N. Jiang, J. Zhou, and B. Ramabhadran, “N-best entropy based data selection for acoustic modeling,” in
2012
Earlier work this paper cites.
G. Saon, H. Soltau, D. Nahamoo, and M. Picheny, “Speaker adaptation of neural network acoustic models using i-vectors,” in
2013
Earlier work this paper cites.
G. E. Dahl, T. N. Sainath, and G. E. Hinton, “Improving deep neural networks for lvcsr using rectified linear units and dropout,” in
2013
Earlier work this paper cites.
K. Wei, Y. Liu, K. Kirchhoff, C. Bartels, and J. Bilmes, “Submodular subset selection for large-scale speech training data,” in
2014
Earlier work this paper cites.
K. Wei, Y. Liu, K. Kirchhoff, and J. Bilmes, “Unsupervised submodular subset selection for speech data,” in
2014
Earlier work this paper cites.
A. Das and M. Hasegawa-Johnson, “Cross-lingual transfer learning during supervised training in low resource scenarios,” in
2015
Earlier work this paper cites.
G. Chen, H. Xu, M. Wu, D. Povey, and S. Khudanpur, “Pronunciation and silence probability modeling for asr,” in
2015
Earlier work this paper cites.
V. Peddinti, D. Povey, and S. Khudanpur, “A time delay neural network architecture for efficient modeling of long temporal contexts,” in
2015
Earlier work this paper cites.
T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, “Audio augmentation for speech recognition,” 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.” in
2016
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
S. Toshniwal, T. N. Sainath, R. J. Weiss, B. Li, P. Moreno, E. Weinstein, and K. Rao, “Multilingual speech recognition with a single end-to-end model,” in
S. Zhou, L. Dong, S. Xu, and B. Xu, “A comparison of modeling units in sequence-to-sequence speech recognition with the transformer on Mandarin Chinese,” in
2018
Later among the works it cites.
Z. Zeng, Y. Khassanov, V. T. Pham, H. Xu, E. S. Chng, and H. Li, “On the end-to-end solution to Mandarin-English code-switching speech recognition,” in
2019
Later among the works it cites.
C. Shan, C. Weng, G. Wang, D. Su, M. Luo, D. Yu, and L. Xie, “Investigating end-to-end speech recognition for Mandarin-English code-switching,” in
2019
Later among the works it cites.
Y. Khassanov, H. Xu, V. T. Pham, Z. Zeng, E. S. Chng, C. Ni, and B. Ma, “Constrained output embeddings for end-to-end code-switching speech recognition with only monolingual data,” in
2019
Later among the works it cites.
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2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. Zeyer, K. Iriel, R. Schlüter, and N. Hermann, “Improved training of end-to-end attention models for speech recognition,” in
2018
Cited alongside, same era.
D. Povey, G. Cheng, Y. Wang, K. Li, H. Xu, M. Yarmohamadi, and S. Khudanpur, “Semi-orthogonal low-rank matrix factorization for deep neural networks,” in
2018
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
V. T. Pham, H. Xu, K. Yerbolat, Z. Zheng, E. S. Chng, C. Ni, B. Ma, and H. Li, “Independent language model architecture for end-to-end asr,” in
2020
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