Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition

R. J. Williams and J. Peng, “An efficient gradient-based algorithm for on-line training of recurrent network trajectories,”

1990

Earlier work this paper cites.

M. J. Gales, “Maximum likelihood linear transformations for HMM-based speech recognition,”

1998

Earlier work this paper cites.

J. G. Fiscus, J. Ajot, N. Radde, and C. Laprun, “Multiple dimension Levenshtein edit distance calculations for evaluating automatic speech recognition systems during simultaneous speech,” in

2006

Earlier work this paper cites.

J. Carletta, “Unleashing the killer corpus: experiences in creating the multi-everything AMI meeting corpus,”

2007

Earlier work this paper cites.

J. Garofalo, D. Graff, D. Paul, and D. Pallett, “CSR-I (WSJ0) complete,”

2007

Earlier work this paper cites.

D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz

2011

Earlier work this paper cites.

N. Jaitly and G. E. Hinton, “Vocal tract length perturbation (VTLP) improves speech recognition,” in

2013

Earlier work this paper cites.

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,”

Original

2013

Earlier work this paper cites.

P. Swietojanski and S. Renals, “Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models,” in

2014

Earlier work this paper cites.

D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,”

Original

2014

Earlier work this paper cites.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,”

Original

2014

Cited alongside, same era.

D. Yu, A. Eversole, M. Seltzer, K. Yao, Z. Huang, B. Guenter, O. Kuchaiev, Y. Zhang, F. Seide, H. Wang

2014

Cited alongside, same era.

H. Sak, A. W. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acoustic modeling.” in

2014

Cited alongside, same era.

T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, “Audio augmentation for speech recognition,” in

2015

Cited alongside, same era.

Y. Zhang, G. Chen, D. Yu, K. Yaco, S. Khudanpur, and J. Glass, “Highway long short-term memory RNNs for distant speech recognition,” in

2016

Cited alongside, same era.

J. Kim, M. El-Khamy, and J. Lee, “Residual LSTM: Design of a deep recurrent architecture for distant speech recognition,”

Original

2017

Later among the works it cites.

S. Sun, B. Zhang, L. Xie, and Y. Zhang, “An unsupervised deep domain adaptation approach for robust speech recognition,”

2017

Later among the works it cites.

Z. Meng, Z. Chen, V. Mazalov, J. Li, and Y. Gong, “Unsupervised adaptation with domain separation networks for robust speech recognition,”

Original

2017

Later among the works it cites.

W.-N. Hsu, Y. Zhang, and J. Glass, “Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation,” in

2017

Later among the works it cites.

J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,”