L. R. Rabiner, A tutorial on Hidden Markov Models and selected applications in speech recognition, Proc. of the IEEE, pp.257-285, 1989.

K. Takahashi, H. Yasuda, and T. Matsumoto, A fast HMM algorithm for on-line handwritten character recognition, Proceedings of the Fourth International Conference on Document Analysis and Recognition, pp.4-97, 1997.
DOI : 10.1109/ICDAR.1997.619873

H. Yasuda, T. Talahasjo, and . Matsumoto, A DISCRETE HMM FOR ONLINE HANDWRITING RECOGNITION, International Journal of Pattern Recognition and Artificial Intelligence, vol.14, issue.05, pp.675-688, 2000.
DOI : 10.1142/S021800140000043X

R. Koehle and T. Matsumoto, Pruning algorithms for hmm on-line handwritten recognition, pp.97-102, 1997.

L. E. Baum and T. Petrie, Statistical Inference for Probabilistic Functions of Finite State Markov Chains, The Annals of Mathematical Statistics, vol.37, issue.6, 1966.
DOI : 10.1214/aoms/1177699147

H. Bourland and N. Morgan, Connectionist speech recognition: A hybrid approach, 1994.

A. J. Robinson, An application of recurrent nets to phone probability estimation, IEEE Transactions on Neural Networks, vol.5, issue.2, pp.298-305, 1994.
DOI : 10.1109/72.279192

J. Rottland and G. Rigoll, Tied posteriors: an approach for effective introduction of context dependency in hybrid NN/HMM LVCSR, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), pp.1241-1244, 2000.
DOI : 10.1109/ICASSP.2000.861800

H. Hermansky, D. P. Ellis, and S. Sharma, Tandem connectionist feature extraction for conventional HMM systems, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2000.
DOI : 10.1109/ICASSP.2000.862024

C. Neukirchen and G. , A new approach to hybrid hmm/ann speech recognition using mutual information neural networks, pp.772-778, 1996.

X. D. Huang and M. A. Jack, Semi-continuous hidden markov models for speech signals, Computer Speech and Language, vol.3, pp.1759-1762, 1989.

A. Viterbi, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Transactions on Information Theory, vol.13, issue.2, pp.260-267, 1967.
DOI : 10.1109/TIT.1967.1054010

M. Riedmiller and H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, IEEE International Conference on Neural Networks, 1993.
DOI : 10.1109/ICNN.1993.298623

C. Igel and M. Hüsken, Improving the rprop learning algorithm, Proc. of the Second Int. Symposium on Neural Computation NC'2000, pp.115-121, 2000.

S. Sivadas and H. Hermansky, Hierachical tandem feature extraction, ICASSP, 2002.

G. Rigoll, A. Kosmala, and D. Willett, An investigation of context-dependent and hybrid modeling techniques for very large vocabulary on-line cursive handwriting recognition, pp.429-438, 1998.

G. Rigoll, A. Kosmala, J. Rottland, and C. Neukrichen, A comparison between continous and discrete density hidden markov models for cursive handwriting recognition, Proc. of ICPR '96, pp.205-209, 1996.

A. Brakensiek, A. Kosmala, D. Willet, W. Wang, and G. , Performance evaluation of a new hybrid modeling technique for handwriting recognition using identical on-line and off-line data, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), pp.446-449, 1999.
DOI : 10.1109/ICDAR.1999.791820