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Line 1: Iterative Viterbi Decoding is an algorithm that spots the subsequence S of an observation O={o1o<sub>1</sub>,...,ono<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 [[Bridle]]. An early algorithm to solve this problem, [[sliding window]], was proposed by Wilpon et.al., 1989, with constant cost T=mn^<sup>2</sup>/2. A faster algorithm was developed by Silaghi in 1989 (published 1999). It consists of an iteration of calls to the [[Viterbi algorithm]], reestimating a filler score until convergence.

Iterative Viterbi decoding: Difference between revisions