Rnn-transducer with language bias for end-to-end Mandarin-English code-switching speech recognition

A. Stolcke, “Srilm-an extensible language modeling toolkit,” in Seventh international conference on spoken language processing , 2002

2002

Earlier work this paper cites.

A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in Proceedings of the 23rd international conference on Machine learning . ACM, 2006, pp. 369–376

2006

Earlier work this paper cites.

Y. Li, P. Fung, P. Xu, and Y. Liu, “Asymmetric acoustic modeling of mixed language speech,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2011, pp. 5004–5007

2011

Earlier work this paper cites.

A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE international conference on acoustics, speech and signal processing . IEEE, 2013, pp. 6645–6649

2013

Earlier work this paper cites.

A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in International conference on machine learning , 2014, pp. 1764–1772

2014

Earlier work this paper cites.

D.-C. Lyu, T.-P. Tan, E.-S. Chng, and H. Li, “Mandarin-english code-switching speech corpus in south-east asia: Seame,” Language Resources and Evaluation , vol. 49, no. 3, pp. 581–600, 2015

2015

Earlier work this paper cites.

Ö. Çetinoğlu, S. Schulz, and N. T. Vu, “Challenges of computational processing of code-switching,” arXiv preprint arXiv:1610.02213 , 2016

Original

2016

Earlier work this paper cites.

W. Chan, N. Jaitly, Q. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 4960–4964

2016

Cited alongside, same era.

D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio, “End-to-end attention-based large vocabulary speech recognition,” in 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2016, pp. 4945–4949

2016

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 Interspeech , 2016, pp. 2751–2755

2016

Cited alongside, same era.

K. Rao, H. Sak, and R. Prabhavalkar, “Exploring architectures, data and units for streaming end-to-end speech recognition with rnn-transducer,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2017, pp. 193–199

P. Guo, H. Xu, L. Xie, and E. S. Chng, “Study of semi-supervised approaches to improving english-mandarin code-switching speech recognition,” arXiv preprint arXiv:1806.06200 , 2018

Original

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,” arXiv preprint arXiv:1811.00241 , 2018

Original

2018

Later among the works it cites.

C.-C. Chiu, T. N. Sainath, Y. Wu, R. Prabhavalkar, P. Nguyen, Z. Chen, A. Kannan, R. J. Weiss, K. Rao, E. Gonina et al. , “State-of-the-art speech recognition with sequence-to-sequence models,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 4774–4778

2018

Later among the works it cites.

C. Shan, C. Weng, G. Wang, D. Su, and L. Xie, “Investigating end-to-end speech recognition for mandarin-english code-switching,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019