Fetching the paper…
Reading the bibliography…
Deep Neural Network (DNN) acoustic models have yielded many state-of-the-art results in Automatic Speech Recognition (ASR) tasks.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,”
1997
Earlier work this paper cites.
C. Bucila, R. Caruana, and A. Niculescu-Mizil, “Model Compression,” in
2006
Earlier work this paper cites.
S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber, “Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies,” 2011
2011
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannenmann, P. Motlicek, Y. Qian, P. Schwarz, J. Silovsky, G. Stemmer, and K. Vesely, “The Kaldi Speech Recognition Toolkit,” in
2011
Earlier work this paper cites.
G. E. Dahl, D. Yu, L. Deng, and A. Acero, “Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition,”
2012
Earlier work this paper cites.
L. Deng, J. Li, J.-T. Huang, K. Yao, D. Yu, F. Seide, M. Seltzer, G. Zweig, X. He, J. Williams, Y. Gong, and A. Acero, “Recent advances in deep learning for speech research at microsoft,” May 2013
2013
Earlier work this paper cites.
M. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q. V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, and G. E. Hinton, “On Rectified Linear Units for Speech Processing,” in
2013
Earlier work this paper cites.
G. E. Dahl, T. N. Sainath, and G. E. Hinton, “Improving Deep Neural Networks for LVCSR Using Rectified Linear Units and Dropout,” in
2013
Earlier work this paper cites.
A. Graves, A. rahman Mohamed, and G. Hinton, “Speech Recognition with Deep Recurrent Neural Networks,” in
2013
Earlier work this paper cites.
A. Graves, N. Jaitly, and A. rahman Mohamed, “Hybrid Speech Recognition with Bidirectional LSTM,” in
2013
Cited alongside, same era.
X. Lei, A. Senior, A. Gruenstein, and J. Sorensen, “Accurate and Compact Large Vocabulary Speech Recognition on Mobile Devices,” in
2013
Cited alongside, same era.
K. Vesely, A. Ghoshal, L. Burget, and D. Povey, “Sequence-discriminative training of deep neural networks,” in
2013
Cited alongside, same era.
H. Soltau, G. Saon, and T. Sainath, “Joint Training of Convoutional and Non-Convoutional Neural Networks,” in
2014
Cited alongside, same era.
C. Weng, D. Yu, S. Watanabe, and F. Jung, “Recurrent Deep Neural Networks for Robust Speech Recognition,” in
2014
Cited alongside, same era.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the Knowledge in a Neural Network,” in
2014
Later among the works it cites.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” in
2014
Later among the works it cites.
2014
Later among the works it cites.
R. Pascanu, C. Gulcehre, K. Cho, and Y. Bengio, “How to Construct Deep Recurrent Neural Networks,” in
2014
Later among the works it cites.
2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Sak, A. Senior, and F. Beaufays, “Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling,” in
2014
Cited alongside, same era.
N. Jaitly, V. Vanhoucke, and G. Hinton, “Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models,” in
2014
Cited alongside, same era.
A. Senior, G. Heigold, M. Bacchiani, and H. Liao, “GMM-Free DNN Training,” in
2014
Cited alongside, same era.
J. Li, R. Zhao, J.-T. Huang, and Y. Gong, “Learning Small-Size DNN with Output-Distribution-Based Criteria,” in
2014
Cited alongside, same era.
2014
Later among the works it cites.
W. Chan and I. Lane, “Deep Recurrent Neural Networks for Acoustic Modelling,” in
2015
Closest in time.
W. Chan and I. Lane, “Deep Convolutional Neural Networks for Acoustic Modeling in Low Resource Languages,” in
2015
Closest in time.