Extended Viterbi algorithm for second order hidden Markov process

An extended Viterbi algorithm is presented that gives a maximum a posteriori estimation of the second-order hidden Markov process. The advantage of the second-order model and the complexity of the extended algorithm are compared with those of the original first-order one. The method used to develop the extended algorithm can also be used to extend the Viterbi algorithm further to any higher order.<<ETX>>

[1]  Alan G. Konheim.  Cryptography, a primer , 1981 .

[2]  Jr. G. Forney,et al.  The viterbi algorithm , 1973 .

[3]  A. Kundu,et al.  Recognition of handwritten script: a hidden Markov model based approach , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[4]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[5]  Paramvir Bahl,et al.  Recognition of handwritten word: first and second order hidden Markov model based approach , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Kin Hong Wong,et al.  Script recognition using hidden Markov models , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  L. R. Rabiner,et al.  On the application of vector quantization and hidden Markov models to speaker-independent, isolated word recognition , 1983, The Bell System Technical Journal.