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Recently, there has been significant progress made in Automatic Speech Recognition (ASR) of code-switched speech, leading to gains in accuracy on code-switched datasets in many language pairs.
B. M. L. Srivastava and S. Sitaram, “Homophone identification and merging for code-switched speech recognition.” in
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A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in
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A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in
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2014
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B. Gambäck and A. Das, “On measuring the complexity of code-mixing,” in
2014
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M. Choudhury, K. Bali, S. Sitaram, and A. Baheti, “Curriculum design for code-switching: Experiments with language identification and language modeling with deep neural networks,” in
2017
Cited alongside, same era.
X. Song, Y. Liu, D. Yang, and Y. Zou, “A multi-task learning approach for mandarin-english code-switching conversational speech recognition,” 2017
2017
Cited alongside, same era.
Z. Li and D. Hoiem, “Learning without forgetting,”
2018
Cited alongside, same era.
J. Li, G. Ye, A. Das, R. Zhao, and Y. Gong, “Advancing acoustic-to-word ctc model,” in
2018
Cited alongside, same era.
2018
Cited alongside, same era.
G. Winata, A. Madotto, C.-S. Wu, and P. Fung, “Towards end-to-end automatic code-switching speech recognition,” 10 2018
2018
Later among the works it cites.
2019
Later among the works it cites.
A. Das, J. Li, G. Ye, R. Zhao, and Y. Gong, “Advancing acoustic-to-word ctc model with attention and mixed-units,”
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.
2020
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