Tara Sainath
Tara Sainath
Principal Research Scientist, Google
Verified email at - Homepage
Cited by
Cited by
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G Hinton, L Deng, D Yu, GE Dahl, A Mohamed, N Jaitly, A Senior, ...
IEEE Signal processing magazine 29 (6), 82-97, 2012
Deep convolutional neural networks for large-scale speech tasks
TN Sainath, B Kingsbury, G Saon, H Soltau, A Mohamed, G Dahl, ...
Neural networks 64, 39-48, 2015
Improving deep neural networks for LVCSR using rectified linear units and dropout
GE Dahl, TN Sainath, GE Hinton
2013 IEEE international conference on acoustics, speech and signal …, 2013
Convolutional, long short-term memory, fully connected deep neural networks
TN Sainath, O Vinyals, A Senior, H Sak
2015 IEEE international conference on acoustics, speech and signal …, 2015
State-of-the-art speech recognition with sequence-to-sequence models
CC Chiu, TN Sainath, Y Wu, R Prabhavalkar, P Nguyen, Z Chen, ...
2018 IEEE international conference on acoustics, speech and signal …, 2018
Low-rank matrix factorization for deep neural network training with high-dimensional output targets
TN Sainath, B Kingsbury, V Sindhwani, E Arisoy, B Ramabhadran
2013 IEEE international conference on acoustics, speech and signal …, 2013
Streaming end-to-end speech recognition for mobile devices
Y He, TN Sainath, R Prabhavalkar, I McGraw, R Alvarez, D Zhao, ...
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019
Deep learning for audio signal processing
H Purwins, B Li, T Virtanen, J Schlüter, SY Chang, T Sainath
IEEE Journal of Selected Topics in Signal Processing 13 (2), 206-219, 2019
Convolutional neural networks for small-footprint keyword spotting
T Sainath, C Parada
Learning the speech front-end with raw waveform CLDNNs
T Sainath, RJ Weiss, K Wilson, AW Senior, O Vinyals
Deep belief networks using discriminative features for phone recognition
A Mohamed, TN Sainath, G Dahl, B Ramabhadran, GE Hinton, ...
2011 IEEE international conference on acoustics, speech and signal …, 2011
A Comparison of sequence-to-sequence models for speech recognition.
R Prabhavalkar, K Rao, TN Sainath, B Li, L Johnson, N Jaitly
Interspeech, 939-943, 2017
Improvements to deep convolutional neural networks for LVCSR
TN Sainath, B Kingsbury, A Mohamed, GE Dahl, G Saon, H Soltau, ...
2013 IEEE workshop on automatic speech recognition and understanding, 315-320, 2013
Deep neural network language models
E Arisoy, TN Sainath, B Kingsbury, B Ramabhadran
Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the …, 2012
Scalable minimum Bayes risk training of deep neural network acoustic models using distributed Hessian-free optimization
B Kingsbury, TN Sainath, H Soltau
Thirteenth annual conference of the international speech communication …, 2012
Structured transforms for small-footprint deep learning
V Sindhwani, T Sainath, S Kumar
Advances in Neural Information Processing Systems 28, 2015
Multilingual speech recognition with a single end-to-end model
S Toshniwal, TN Sainath, RJ Weiss, B Li, P Moreno, E Weinstein, K Rao
2018 IEEE international conference on acoustics, speech and signal …, 2018
Generation of large-scale simulated utterances in virtual rooms to train deep-neural networks for far-field speech recognition in Google Home
C Kim, A Misra, K Chin, T Hughes, A Narayanan, T Sainath, M Bacchiani
Multichannel signal processing with deep neural networks for automatic speech recognition
TN Sainath, RJ Weiss, KW Wilson, B Li, A Narayanan, E Variani, ...
IEEE/ACM Transactions on Audio, Speech, and Language Processing 25 (5), 965-979, 2017
Making deep belief networks effective for large vocabulary continuous speech recognition
TN Sainath, B Kingsbury, B Ramabhadran, P Fousek, P Novak, ...
2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 30-35, 2011
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