Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2015-August, Issue , 2015, Pages 4580-4584

  Sainath, Tara N ^a   Vinyals, Oriol ^a   Senior, Andrew ^a   Sak, Hasim ^a  

^a GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUDIO SIGNAL PROCESSING; BRAIN; CONVOLUTION; DEEP NEURAL NETWORKS; MEMORY ARCHITECTURE; NETWORK ARCHITECTURE; SPEECH COMMUNICATION; SPEECH RECOGNITION;

CONVOLUTIONAL NEURAL NETWORK; FREQUENCY VARIATION; INDIVIDUAL MODELS; LARGE VOCABULARY; PROPOSED ARCHITECTURES; TEMPORAL MODELING; UNIFIED ARCHITECTURE;

LONG SHORT-TERM MEMORY;

EID: 84946037134     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2015.7178838     Document Type: Conference Paper

References (20)

1. G. Hinton, L. Deng, D. Yu, G. 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, IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, 2012
2. T. N. Sainath, A. Mohamed, B. Kingsbury, and B. Ramabhadran, Deep Convolutional Neural Networks for LVCSR, in Proc. ICASSP, 2013
3. H. Sak, A. Senior, and F. Beaufays, Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling, in Proc. Interspeech, 2014
4. R. Pascanu, C. Gulcehre, K. Cho, and Y. Bengio, How to Construct Deep Recurrent Neural Networks, in Proc. ICLR, 2014
5. A. Mohamed, G. Hinton, and G. Penn, Understanding how Deep Belief Networks Perform Acoustic Modelling, in ICASSP, 2012
6. H. Soltau, G. Saon, and B. Kingsbury, The IBM Attila speech recognition toolkit, in Proc. IEEE Workshop on Spoken Language Technology, 2010
7. T. N. Sainath, B. Ramabhadran, M. Picheny, D. Nahamoo, and D. Kanevsky, Exemplar-Based Sparse Representation Features: From TIMIT to LVCSR, in IEEE TSAP, 2011
8. G. Saon, H. Soltau, A. Emami, and M. Picheny, Unfolded Recurrent Neural Networks for Speech Recognition, in Interspeech, 2014
9. L. Deng and J. Platt, Ensemble Deep Learning for Speech Recognition, in Proc. Interspeech, 2014
10. P. Sermanet and Y. LeCun, Traffic sign recognition with multi-scale convolutional networks, in Proc. IJCNN, 2011
11. T. N. Sainath, B. Kingsbury, A. Mohamed, G. Dahl, G. Saon, H. Soltau, T. Beran, A. Aravkin, and B. Ramabhadran, Improvements to Deep Convolutional Neural Networks for LVCSR, in in Proc. ASRU, 2013
12. T. N. Sainath, V. Peddinti, B. Kingsbury, P. Fousek, D. Nahamoo, and B. Ramabhadhran, Deep Scattering Spectra with Deep Neural Networks for LVCSR Tasks, in Proc. Interspeech, 2014
13. P. Schwarz, P. Matejka, and J. Cernocky, Hierarchical Structures of Neural Networks for Phoneme Recognition, in Proc. ICASSP, 2006
14. F. Grezl and M. Karafat, Semi-Supervised Bootstrapping Approach for Neural Network Feature Extractor Training, in Proc. ASRU, 2013
15. H. Soltau, G. Saon, and T. N. Sainath, Joint Training of Convolutional and Non-Convolutional Neural Networks, in in Proc. ICASSP, 2014
16. J. Dean, G.S. Corrado, R. Monga, K. Chen, M. Devin, Q.V. Le, M.Z. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, and A.Y. Ng, Large Scale Distributed Deep Networks, in Proc. NIPS, 2012
17. G. Heigold, E. McDermott, V. Vanhoucke, A. Senior, and M. Bacchiani, Asynchronous Stochastic Optimization for Sequence Training of Deep Neural Networks, in Proc. ICASSP, 2014
18. X. Glorot and Y. Bengio, Understanding the Difficulty of Training Deep Feedforward Neural Networks, in Proc. AISTATS, 2014
19. R. Pascanu, T. Mikolov, and Y. Bengio, On the Difficulty of Training Recurrent Neural Networks, in Proc. ICML, 2013
20. B. Kingsbury, Lattice-Based Optimization of Sequence Classification Criteria for Neural-Network Acoustic Modeling, in Proc. ICASSP, 2009