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L. R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition,”
1989
Earlier work this paper cites.
P. Gage, “A new algorithm for data compression,”
1994
Earlier work this paper cites.
M. Finke and A. Waibel, “Speaking mode dependent pronunciation modeling in large vocabulary conversational speech recognition.” in
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
J. G. Fiscus, “A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (rover),” in
Original
1997
Earlier work this paper cites.
A. Graves, S. Fernández, and J. Schmidhuber, “Bidirectional lstm networks for improved phoneme classification and recognition,”
2005
Earlier work this paper cites.
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in
2006
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T. Mikolov, I. Sutskever, A. Deoras, H.-S. Le, S. Kombrink, and J. Cernocky, “Subword language modeling with neural networks,” 2012
2012
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A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in
2014
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2017

Zenkel, Thomas, Sanabria, Ramon, Metze, Florian et al.

This paper proposes a novel approach to create an unit set for CTC based speech recognition systems.

By using Byte Pair Encoding we learn an unit set of an arbitrary size on a given training text.
In contrast to using characters or words as units this allows us to find a good trade-off between the size of our unit set and the available training data.
We evaluate both Crossword units, that may span multiple word, and Subword units.

L. R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition,”
1989
Earlier work this paper cites.
P. Gage, “A new algorithm for data compression,”
1994
Earlier work this paper cites.
M. Finke and A. Waibel, “Speaking mode dependent pronunciation modeling in large vocabulary conversational speech recognition.” in
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
J. G. Fiscus, “A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (rover),” in
Original
1997
Earlier work this paper cites.
A. Graves, S. Fernández, and J. Schmidhuber, “Bidirectional lstm networks for improved phoneme classification and recognition,”
2005
Earlier work this paper cites.
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.
T. Mikolov, I. Sutskever, A. Deoras, H.-S. Le, S. Kombrink, and J. Cernocky, “Subword language modeling with neural networks,” 2012
2012
Earlier work this paper cites.
A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in
2014
Earlier work this paper cites.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
Original
2014
Cited alongside, same era.
A. L. Maas, Z. Xie, D. Jurafsky, and A. Y. Ng, “Lexicon-free conversational speech recognition with neural networks.” in
2015
Cited alongside, same era.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,”
Original
2015
Cited alongside, same era.
Y. Miao, M. Gowayyed, and F. Metze, “Eesen: End-to-end speech recognition using deep rnn models and wfst-based decoding,” in
2015
Cited alongside, same era.
W. Chan, N. Jaitly, Q. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in
2016
Cited alongside, same era.