  We describe a new framework for distilling information from word lattices to
improve the accuracy of speech recognition and obtain a more perspicuous
representation of a set of alternative hypotheses. In the standard MAP decoding
approach the recognizer outputs the string of words corresponding to the path
with the highest posterior probability given the acoustics and a language
model. However, even given optimal models, the MAP decoder does not necessarily
minimize the commonly used performance metric, word error rate (WER). We
describe a method for explicitly minimizing WER by extracting word hypotheses
with the highest posterior probabilities from word lattices. We change the
standard problem formulation by replacing global search over a large set of
sentence hypotheses with local search over a small set of word candidates. In
addition to improving the accuracy of the recognizer, our method produces a new
representation of the set of candidate hypotheses that specifies the sequence
of word-level confusions in a compact lattice format. We study the properties
of confusion networks and examine their use for other tasks, such as lattice
compression, word spotting, confidence annotation, and reevaluation of
recognition hypotheses using higher-level knowledge sources.
