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Iterative Viterbi Decoding is an algorithm that spots the subsequence S of an observation O={o<sub>1</sub>,...,o<sub>n</sub>} having the highest average probability (i.e., probability scaled by the length of S) of being generated by a given Hidden Markov Model M with m states. The algorithm uses a modified [[Viterbi algorithm]] as an internal step.
The scaled probability measure was first proposed by [[John S. Bridle]]. An early algorithm to solve this problem, [[sliding window]], was proposed by [[Jay G. Wilpon]] et
A faster algorithm was developed by [[Marius C. Silaghi]] in 1998 (published 1999). It consists of an iteration of calls to the [[Viterbi algorithm]], reestimating a filler score until convergence.
== The
A basic (non-optimized) version looks like:
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== References ==
* Silaghi,
* Silaghi,
* Silaghi,
*
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