-
2
-
-
80051616844
-
Large vocabulary continuous speech recognition with context-dependent DBN-HMMs
-
G. E. Dahl, D. Yu, and L. Deng. 2011. Large vocabulary continuous speech recognition with context-dependent DBN-HMMs. In Proc. ICASSP.
-
(2011)
Proc. ICASSP
-
-
Dahl, G.E.1
Yu, D.2
Deng, L.3
-
3
-
-
84890527827
-
Improving deep neural networks for lvcsr using rectified linear units and dropout
-
G. E. Dahl, T. N. Sainath, and G. E. Hinton. 2013. Improving Deep Neural Networks for LVCSR using Rectified Linear Units and Dropout. In ICASSP.
-
(2013)
ICASSP
-
-
Dahl, G.E.1
Sainath, T.N.2
Hinton, G.E.3
-
4
-
-
85032750869
-
Spoken language understanding
-
R. De Mori, F. Bechet, D. Hakkani-Tur, M. McTear, G. Riccardi, and G. Tur. 2008. Spoken language understanding. Signal Processing Magazine, IEEE, 25(3):50-58.
-
(2008)
Signal Processing Magazine, IEEE
, vol.25
, Issue.3
, pp. 50-58
-
-
De Mori, R.1
Bechet, F.2
Hakkani-Tur, D.3
McTear, M.4
Riccardi, G.5
Tur, G.6
-
5
-
-
84862294866
-
Deep sparse rectifier networks
-
X. Glorot, A. Bordes, and Y Bengio. 2011. Deep Sparse Rectifier Networks. In AISTATS, pages 315-323.
-
(2011)
AISTATS
, pp. 315-323
-
-
Glorot, X.1
Bordes, A.2
Bengio, Y.3
-
6
-
-
73649124909
-
Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates
-
S. Goldwater, D. Jurafsky, and C. Manning. 2010. Which Words are Hard to Recognize? Prosodic, Lexical, and Disfluency Factors That Increase Speech Recognition Error Rates. Speech Communications, 52:181-200.
-
(2010)
Speech Communications
, vol.52
, pp. 181-200
-
-
Goldwater, S.1
Jurafsky, D.2
Manning, C.3
-
7
-
-
84919832465
-
Towards end-to-end speech recognition with recurrent neural networks
-
A. Graves and N. Jaitly. 2014. Towards End-to-End Speech Recognition with Recurrent Neural Networks. In ICML.
-
(2014)
ICML
-
-
Graves, A.1
Jaitly, N.2
-
8
-
-
33749259827
-
Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks
-
ACM
-
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber. 2006. Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In ICML, pages 369-376. ACM.
-
(2006)
ICML
, pp. 369-376
-
-
Graves, A.1
Fernández, S.2
Gomez, F.3
Schmidhuber, J.4
-
9
-
-
84907337539
-
Scalable modified Kneser-Ney language model estimation
-
Sofia, Bulgaria
-
K. Heafield, I. Pouzyrevsky, J. H. Clark, and P. Koehn. 2013. Scalable modified Kneser-Ney language model estimation. In ACL-HLT, pages 690-696, Sofia, Bulgaria.
-
(2013)
ACL-HLT
, pp. 690-696
-
-
Heafield, K.1
Pouzyrevsky, I.2
Clark, J.H.3
Koehn, P.4
-
10
-
-
85032751458
-
Deep neural networks for acoustic modeling in speech recognition
-
November
-
G. E. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury. 2012. Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 29(November):82-97.
-
(2012)
IEEE Signal Processing Magazine
, vol.29
, pp. 82-97
-
-
Hinton, G.E.1
Deng, L.2
Yu, D.3
Dahl, G.E.4
Mohamed, A.5
Jaitly, N.6
Senior, A.7
Vanhoucke, V.8
Nguyen, P.9
Sainath, T.10
Kingsbury, B.11
-
13
-
-
79959829092
-
Recurrent neural network based language model
-
T. Mikolov, M. Karafiát, L. Burget, J. Cernocky, and S. Khudanpur. 2010. Recurrent neural network based language model. In INTERSPEECH, pages 1045-1048.
-
(2010)
INTERSPEECH
, pp. 1045-1048
-
-
Mikolov, T.1
Karafiát, M.2
Burget, L.3
Cernocky, J.4
Khudanpur, S.5
-
14
-
-
84893696682
-
The kaldi speech recognition toolkit
-
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, K. Veselý, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz, J. Silovsky, and G. Stemmer. 2011. The kaldi speech recognition toolkit. In ASRU.
-
(2011)
ASRU
-
-
Povey, D.1
Ghoshal, A.2
Boulianne, G.3
Burget, L.4
Glembek, O.5
Veselý, K.6
Goel, N.7
Hannemann, M.8
Motlicek, P.9
Qian, Y.10
Schwarz, P.11
Silovsky, J.12
Stemmer, G.13
-
15
-
-
84910038108
-
Parallel deep neural network training for lvcsr tasks using blue gene/q
-
T. N. Sainath, I. Chung, B. Ramabhadran, M. Picheny, J. Gunnels, B. Kingsbury, G. Saon, V. Austel, and U. Chaudhari. 2014. Parallel deep neural network training for lvcsr tasks using blue gene/q. In INTERSPEECH.
-
(2014)
INTERSPEECH
-
-
Sainath, T.N.1
Chung, I.2
Ramabhadran, B.3
Picheny, M.4
Gunnels, J.5
Kingsbury, B.6
Saon, G.7
Austel, V.8
Chaudhari, U.9
-
16
-
-
85032751472
-
Large-vocabulary continuous speech recognition systems: A look at some recent advances
-
G. Saon and J. Chien. 2012. Large-vocabulary continuous speech recognition systems: A look at some recent advances. IEEE Signal Processing Magazine, 29(6):18-33.
-
(2012)
IEEE Signal Processing Magazine
, vol.29
, Issue.6
, pp. 18-33
-
-
Saon, G.1
Chien, J.2
-
17
-
-
80053459857
-
Generating text with recurrent neural networks
-
I. Sutskever, J. Martens, and G. E. Hinton. 2011. Generating text with recurrent neural networks. In ICML, pages 1017-1024.
-
(2011)
ICML
, pp. 1017-1024
-
-
Sutskever, I.1
Martens, J.2
Hinton, G.E.3
-
18
-
-
84892623436
-
On the importance of momentum and initialization in deep learning
-
I. Sutskever, J. Martens, G. Dahl, and G. Hinton. 2013. On the Importance of Momentum and Initialization in Deep Learning. In ICML.
-
(2013)
ICML
-
-
Sutskever, I.1
Martens, J.2
Dahl, G.3
Hinton, G.4
-
20
-
-
84890471125
-
On rectified linear units for speech processing
-
M. D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, and G. E. Hinton. 2013. On Rectified Linear Units for Speech Processing. In ICASSP.
-
(2013)
ICASSP
-
-
Zeiler, M.D.1
Ranzato, M.2
Monga, R.3
Mao, M.4
Yang, K.5
Le, Q.V.6
Nguyen, P.7
Senior, A.8
Vanhoucke, V.9
Dean, J.10
Hinton, G.E.11
|