Streaming automatic speech recognition with the transformer model

“CSR-II (WSJ1) complete,” vol. LDC94S13A. Philadelphia: Linguistic Data Consortium, 1994

1994

Earlier work this paper cites.

A. Graves, S. Fernández, F. J. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in Proc. International Conference on Machine Learning (ICML) , vol. 148, Jun. 2006, pp. 369–376

2006

Earlier work this paper cites.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Processing Magazine , vol. 29, no. 6, pp. 82–97, 2012

2012

Earlier work this paper cites.

A. Graves, “Sequence transduction with recurrent neural networks,” arXiv preprint arXiv:abs/1211.3711 , 2012

Original

2012

Earlier work this paper cites.

D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:abs/1409.0473 , 2014

Original

2014

Earlier work this paper cites.

A. L. Maas, A. Y. Hannun, D. Jurafsky, and A. Y. Ng, “First-pass large vocabulary continuous speech recognition using bi-directional recurrent DNNs,” arXiv preprint arXiv:1408.2873 , 2014

Original

2014

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. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Apr. 2015, pp. 5206–5210

2015

Earlier work this paper cites.

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 Proc. ISCA Interspeech , Sep. 2016, pp. 2751–2755

2016

Earlier work this paper cites.

R. Prabhavalkar, K. Rao, T. N. Sainath, B. Li, L. Johnson, and N. Jaitly, “A comparison of sequence-to-sequence models for speech recognition,” in Proc. ISCA Interspeech , Sep. 2017, pp. 939–943

2017

Earlier work this paper cites.

T. N. Sainath, C. Chiu, R. Prabhavalkar, A. Kannan, Y. Wu, P. Nguyen, and Z. Chen, “Improving the performance of online neural transducer models,” arXiv preprint arXiv:abs/1712.01807 , 2017

Original

2017

Cited alongside, same era.

S. Watanabe, T. Hori, S. Kim, J. R. Hershey, and T. Hayashi, “Hybrid CTC/attention architecture for end-to-end speech recognition,” J. Sel. Topics Signal Processing , vol. 11, no. 8, pp. 1240–1253, 2017

2017

Cited alongside, same era.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. NIPS , Dec. 2017, pp. 6000–6010

2017

Cited alongside, same era.

T. Hori, S. Watanabe, Y. Zhang, and W. Chan, “Advances in joint CTC-attention based end-to-end speech recognition with a deep CNN encoder and RNN-LM,” in Proc. ISCA Interspeech , Aug. 2017, pp. 949–953

2017

Cited alongside, same era.

S. Karita, N. Yalta, S. Watanabe, M. Delcroix, A. Ogawa, and T. Nakatani, “Improving transformer-based end-to-end speech recognition with connectionist temporal classification and language model integration,” in Proc. ISCA Interspeech , Sep. 2019, pp. 1408–1412

2019

Later among the works it cites.

J. Schalkwyk, “An all-neural on-device speech recognizer,” Mar. 2019, url: https://ai.googleblog.com/2019/03/an-all-neural-on-device-speech.html

2019

Later among the works it cites.

J. Li, R. Zhao, H. Hu, and Y. Gong, “Improving RNN transducer modeling for end-to-end speech recognition,” arXiv preprint arXiv:abs/1909.12415 , 2019

Original

2019

Later among the works it cites.

N. Moritz, T. Hori, and J. Le Roux, “Triggered attention for end-to-end speech recognition,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , May 2019, pp. 5666–5670

2019

Later among the works it cites.