Reducing Streaming ASR Model Delay with Self Alignment

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 , 2006, pp. 369–376

2006

Earlier work this paper cites.

A. Graves, “Sequence transduction with recurrent neural networks,” in Proceedings of the 29th International Conference on Machine Learning , 2012

2012

Earlier work this paper cites.

T. N. Sainath, O. Vinyals, A. Senior, and H. Sak, “Convolutional, long short-term memory, fully connected deep neural networks,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2015, pp. 4580–4584

2015

Earlier work this paper cites.

H. Sak, F. de Chaumont Quitry, T. Sainath, K. Rao et al. , “Acoustic modelling with cd-ctc-smbr lstm rnns,” in 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) . IEEE, 2015, pp. 604–609

2015

Earlier work this paper cites.

V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: an asr corpus based on public domain audio books,” in Proc. ICASSP . IEEE, 2015, pp. 5206–5210

2015

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008

2017

Cited alongside, same era.

C.-F. Yeh, J. Mahadeokar, K. Kalgaonkar, Y. Wang, D. Le, M. Jain, K. Schubert, C. Fuegen, and M. L. Seltzer, “Transformer-transducer: End-to-end speech recognition with self-attention,” 2019

2019

Cited alongside, same era.

D. S. Park, W. Chan, Y. Zhang, C.-C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le, “Specaugment: A simple data augmentation method for automatic speech recognition,” arXiv preprint arXiv:1904.08779 , 2019

Original

2019

Cited alongside, same era.

Q. Zhang, H. Lu, H. Sak, A. Tripathi, E. McDermott, S. Koo, and S. Kumar, “Transformer transducer: A streamable speech recognition model with transformer encoders and rnn-t loss,” 2020

2020

Cited alongside, same era.

A. Tripathi, J. Kim, Q. Zhang, H. Lu, and H. Sak, “Transformer transducer: One model unifying streaming and non-streaming speech recognition,” arXiv preprint arXiv:2010.03192 , 2020

Original

2020

Later among the works it cites.

T. N. Sainath, R. Pang, D. Rybach, B. Garcıa, and T. Strohman, “Emitting word timings with end-to-end models,” Proc. Interspeech 2020 , pp. 3615–3619, 2020

2020

Later among the works it cites.

H. Inaguma, Y. Gaur, L. Lu, J. Li, and Y. Gong, “Minimum latency training strategies for streaming sequence-to-sequence asr,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6064–6068

2020

Later among the works it cites.

J. Yu, C.-C. Chiu, B. Li, S.-y. Chang, T. N. Sainath, Y. He, A. Narayanan, W. Han, A. Gulati, Y. Wu et al. , “Fastemit: Low-latency streaming asr with sequence-level emission regularization,” arXiv preprint arXiv:2010.11148 , 2020

Original

2020

Later among the works it cites.