Online Keyword Spotting with a Character-Level Recurrent Neural Network

D. Fry, “Simple reaction-times to speech and non-speech stimuli,” Cortex , vol. 11, no. 4, pp. 355–360, 1975

1975

Earlier work this paper cites.

R. C. Rose and D. B. Paul, “A hidden Markov model based keyword recognition system,” in Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on . IEEE, 1990, pp. 129–132

1990

Earlier work this paper cites.

J. S. Bridle, “Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition,” in Neurocomputing . Springer, 1990, pp. 227–236

1990

Earlier work this paper cites.

R. J. Williams and J. Peng, “An efficient gradient-based algorithm for on-line training of recurrent network trajectories,” Neural Computation , vol. 2, no. 4, pp. 490–501, 1990

1990

Earlier work this paper cites.

D. B. Paul and J. M. Baker, “The design for the Wall Street Journal-based CSR corpus,” in Proceedings of the workshop on Speech and Natural Language . Association for Computational Linguistics, 1992, pp. 357–362

1992

Earlier work this paper cites.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997

1997

Earlier work this paper cites.

J. Sohn, N. S. Kim, and W. Sung, “A statistical model-based voice activity detection,” Signal Processing Letters, IEEE , vol. 6, no. 1, pp. 1–3, 1999

1999

Cited alongside, same era.

F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with LSTM,” Neural computation , vol. 12, no. 10, pp. 2451–2471, 2000

2000

Cited alongside, same era.

F. A. Gers, N. N. Schraudolph, and J. Schmidhuber, “Learning precise timing with LSTM recurrent networks,” The Journal of Machine Learning Research , vol. 3, pp. 115–143, 2003

2003

Cited alongside, same era.

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

Cited alongside, same era.

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

2012

Later among the works it cites.

G. Chen, C. Parada, and G. Heigold, “Small-footprint keyword spotting using deep neural networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on . IEEE, 2014, pp. 4087–4091

2014

Later among the works it cites.

A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in Proceedings of the 31st International Conference on Machine Learning (ICML-14) , 2014, pp. 1764–1772

2014

Later among the works it cites.

K. Hwang and W. Sung, “Online sequence training of recurrent neural networks with connectionist temporal classification,” arXiv preprint arXiv:1511.06841 , 2015

Original