Viterbi algorithm: Difference between revisions

Revision as of 14:54, 9 January 2005

Revision as of 12:44, 15 April 2005

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With the algorithm called [[Iterative Viterbi Decoding]] one can find the subsequence of an observation that matches best (on average) to a given HMM. [[Iterative Viterbi Decoding]], developed by M.C.Silaghi (1998) works by iterating the call to a modified Viterbi algorithm, reestimating the score for a filler until convergence.

An alternate algorithm, called the Lazy Viterbi algorithm, has been recently proposed. This works by not expanding any nodes until it really needs to, and usually manages to get away with doing a lot less work (in software) than the ordinary Viterbi algorithm for the same result - however, it is not so easy to parallelize in hardware.

==References==

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* L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. ''Proceedings of the IEEE'' 77(2):257–286, February 1989. (Describes the forward algorithm and Viterbi algorithm for HMMs).

* J Feldman, I Abou-Faycal and M Frigo. A Fast Maximum-Likelihood Decoder for Convolutional Codes.

==External links==