Built on

A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in
2006
Earlier work this paper cites.
M. Ranzato, F. J. Huang, Y. L. Boureau, and Y. LeCun, “Unsupervised learning of invariant feature hierarchies with applications to object recognition,” in
2007
Earlier work this paper cites.
N. Jaitly and G. E. Hinton, “Learning a better representation of speech soundwaves using restricted boltzmann machines.” in
2011
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. Dahl, A. rahman Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,”
2012
Earlier work this paper cites.
C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling,”
2013
Earlier work this paper cites.
K. Heafield, I. Pouzyrevsky, J. H. Clark, and P. Koehn, “Scalable modified Kneser-Ney language model estimation,” in
2013
Earlier work this paper cites.

Similar

Z. Tüske, P. Golik, R. Schlüter, and H. Ney, “Acoustic modeling with deep neural networks using raw time signal for lvcsr,” in
2014
Cited alongside, same era.
S. Dieleman and B. Schrauwen, “End-to-end learning for music audio,” in
2014
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,”
Original
2014
Cited alongside, same era.
N. Neverova, C. Wolf, G. W. Taylor, and F. Nebout, “Multi-scale deep learning for gesture detection and localization,” in
2014
Cited alongside, same era.
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates
Original
2014
Cited alongside, same era.

Then

D. Amodei, R. Anubhai, E. Battenberg, C. Case, J. Casper, B. C. Catanzaro, J. Chen, M. Chrzanowski, A. Coates, G. Diamos, E. Elsen, J. Engel, L. Fan, C. Fougner, T. Han, A. Y. Hannun, B. Jun, P. LeGresley, L. Lin, S. Narang, A. Y. Ng, S. Ozair, R. Prenger, J. Raiman, S. Satheesh, D. Seetapun, S. Sengupta, Y. Wang, Z. Wang, C. Wang, B. Xiao, D. Yogatama, J. Zhan, and Z. Zhu, “Deep speech 2: End-to-end speech recognition in english and mandarin,”
Original
2015
Later among the works it cites.
D. Palaz, R. Collobert
2015
Later among the works it cites.
T. N. Sainath, R. J. Weiss, A. Senior, K. W. Wilson, and O. Vinyals, “Learning the speech front-end with raw waveform cldnns,” in
2015
Later among the works it cites.
P. Golik, Z. Tüske, R. Schlüter, and H. Ney, “Convolutional neural networks for acoustic modeling of raw time signal in lvcsr,” in
2015
Later among the works it cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,”

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2016

Learning Multiscale Features Directly From Waveforms

Zhu, Zhenyao, Engel, Jesse H., Hannun, Awni

Understand

Deep learning has dramatically improved the performance of speech recognition systems through learning hierarchies of features optimized for the task at hand.

However, true end-to-end learning, where features are learned directly from waveforms, has only recently reached the performance of hand-tailored representations based on the Fourier transform.
In this paper, we detail an approach to use convolutional filters to push past the inherent tradeoff of temporal and frequency resolution that exists for spectral representations.
At increased computational cost, we show that increasing temporal resolution via reduced stride and increasing frequency resolution via additional filters delivers significant performance improvements.

Built on

A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in
2006
Earlier work this paper cites.
M. Ranzato, F. J. Huang, Y. L. Boureau, and Y. LeCun, “Unsupervised learning of invariant feature hierarchies with applications to object recognition,” in
2007
Earlier work this paper cites.
N. Jaitly and G. E. Hinton, “Learning a better representation of speech soundwaves using restricted boltzmann machines.” in
2011
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. Dahl, A. rahman Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,”
2012
Earlier work this paper cites.
C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling,”
2013
Earlier work this paper cites.
K. Heafield, I. Pouzyrevsky, J. H. Clark, and P. Koehn, “Scalable modified Kneser-Ney language model estimation,” in
2013
Earlier work this paper cites.

Similar

Z. Tüske, P. Golik, R. Schlüter, and H. Ney, “Acoustic modeling with deep neural networks using raw time signal for lvcsr,” in
2014
Cited alongside, same era.
S. Dieleman and B. Schrauwen, “End-to-end learning for music audio,” in
2014
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,”
Original
2014
Cited alongside, same era.
N. Neverova, C. Wolf, G. W. Taylor, and F. Nebout, “Multi-scale deep learning for gesture detection and localization,” in
2014
Cited alongside, same era.
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates
Original
2014
Cited alongside, same era.

Then

D. Amodei, R. Anubhai, E. Battenberg, C. Case, J. Casper, B. C. Catanzaro, J. Chen, M. Chrzanowski, A. Coates, G. Diamos, E. Elsen, J. Engel, L. Fan, C. Fougner, T. Han, A. Y. Hannun, B. Jun, P. LeGresley, L. Lin, S. Narang, A. Y. Ng, S. Ozair, R. Prenger, J. Raiman, S. Satheesh, D. Seetapun, S. Sengupta, Y. Wang, Z. Wang, C. Wang, B. Xiao, D. Yogatama, J. Zhan, and Z. Zhu, “Deep speech 2: End-to-end speech recognition in english and mandarin,”
Original
2015
Later among the works it cites.
D. Palaz, R. Collobert
2015
Later among the works it cites.
T. N. Sainath, R. J. Weiss, A. Senior, K. W. Wilson, and O. Vinyals, “Learning the speech front-end with raw waveform cldnns,” in
2015
Later among the works it cites.
P. Golik, Z. Tüske, R. Schlüter, and H. Ney, “Convolutional neural networks for acoustic modeling of raw time signal in lvcsr,” in
2015
Later among the works it cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,”

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

J. Koutník, K. Greff, F. J. Gomez, and J. Schmidhuber, “A clockwork RNN,”

Original

2014

Cited alongside, same era.

Original

2015

Later among the works it cites.

T. Sercu, C. Puhrsch, B. Kingsbury, and Y. LeCun, “Very deep multilingual convolutional neural networks for LVCSR,”

Original

2015

Later among the works it cites.