Iterative Viterbi decoding: Difference between revisions

Revision as of 04:54, 14 October 2006

Revision as of 21:55, 24 May 2007

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'''Iterative Viterbi Decodingdecoding''' is an [[algorithm]] that spots the subsequence S of an observation O={o₁,...,o_n} 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 al., 1989, with constant cost ''T'' = ''mn''²/2.

A faster algorithm consists of an iteration of calls to the [[Viterbi algorithm]], reestimating a filler score until convergence.

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== The algorithm ==

A basic (non-optimized) version, finding the sequence ''s'' with the smallest normalized distance from some subsequence of ''t'' is:

<pre>

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</pre>

The ViterbiDistance() procedure returns the tuple (''e'', ''B'', ''E''), i.e., the Viterbi score "''e''" for the match of ''t'' and the selected entry (''B'') and exit (''E'') points from it. "''B''" and "''E''" have to be recorded using a simple modification to Viterbi.

A modification that can be applied to CYK tables, proposed by Antoine Rozenknop, consists in subtracting ''e'' from all elements of the initial matrix ''d''.

== References ==