Recently there has been a lot of interest in confusion network re-scoring using
sophisticated and complex knowledge sources. Traditionally, re-scoring has been carried
out by the N-best list method or by the lattices or confusion network dynamic programming
method. Although the dynamic programming method is optimal, it allows for the
incorporation of only Markov knowledge sources. N-best lists, on the other hand, can
incorporate sentence level knowledge sources, but with increasing N, the re-scoring
becomes computationally very intensive. In this paper, we present an elegant framework
for confusion network re-scoring called ’Iterative Decoding’. In it, integration of multiple
and complex knowledge sources is not only easier but it also allows for much faster re-
scoring as compared to the N-best list method. Experiments with Language Model re-scoring
show that for comparable performance (in terms of word error rate (WER)) of Iterative
Decoding and N-best list re-scoring, the search effort required by our method is 22 times
less than that of the N-best list method.