Framewise phoneme classification with bidirectional LSTM and other neural network architectures

Baldi, 1999, Exploiting the past and the future in protein secondary structure prediction, BIOINF: Bioinformatics, 15

Baldi, 2001, Bidirectional dynamics for protein secondary structure prediction, Lecture Notes in Computer Science, 1828, 80, 10.1007/3-540-44565-X_5

Beringer, 2004

Beringer, 2004

Bishop, 1995

Bourlard, 1994

Chen, 2004, Capturing long-term dependencies for protein secondary structure prediction, 494

Chen, 1996, Experiments on the implementation of recurrent neural networks for speech phone recognition

Eck, D., Graves, A., & Schmidhuber, J. (2003). A new approach to continuous speech recognition using LSTM recurrent neural networks. Technical Report IDSIA-14-03, IDSIA, www.idsia.ch/techrep.html.

Fukada, 1999, Phoneme boundary estimation using bidirectional recurrent neural networks and its applications, Systems and Computers in Japan, 30, 20, 10.1002/(SICI)1520-684X(199904)30:4<20::AID-SCJ3>3.0.CO;2-E

Garofolo, 1993

Gers, 2002, Learning precise timing with LSTM recurrent networks, Journal of Machine Learning Research, 3, 115

Graves, 2005, Framewise phoneme classification with bidirectional lstm networks

Graves, 2004

Graves, 2004

Graves, A., Beringer, N., & Schmidhuber, J. (2005). Rapid retraining on speech data with lstm recurrent networks. Technical Report IDSIA-09-05, IDSIA, www.idsia.ch/-techrep.html.

Hochreiter, 1997, Long short-term memory, Neural Computation, 9, 1735, 10.1162/neco.1997.9.8.1735

Hochreiter, 2001, Gradient flow in recurrent nets: The difficulty of learning long-term dependencies

Robinson, A. J. (1991). Several improvements to a recurrent error propagation network phone recognition system. Technical Report CUED/F-INFENG/TR82, University of Cambridge.

Robinson, 1994, An application of recurrent nets to phone probability estimation, IEEE Transactions on Neural Networks, 5, 298, 10.1109/72.279192

Robinson, A.J., & Fallside, F. (1987). The utility driven dynamic error propagation network. Technical Report CUED/F-INFENG/TR.1, Cambridge University Engineering Department.

Schuster, M. (1999). On supervised learning from sequential data with applications for speech recognition. PhD thesis, Nara Institute of Science and Technolog, Kyoto, Japan.

Schuster, 1997, Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, 45, 2673, 10.1109/78.650093

Williams, 1995, Gradient-based learning algorithms for recurrent networks and their computational complexity, 433