Lattice Compression in the Consensual Post-Processing Framework

摘要

Word Lattices are used by most speech recognizers as a compact representation of a set of alternative hypotheses. In large-vocabulary, multi-pass recognition systems it is important to generate word lattices incorporating a large number of hypotheses but at the same time keeping the size of the representation as small as possible. Previously we presented a method for identifying mutually supporting and competing word hypotheses in a recognition lattice. In this paper we show how the outcome of this method can be used for compressing lattices. The success of the new technique comes from the ability to discard links with low a posteriori probability and recornbine the remaining ones to create a new set of hypotheses. Experiments on the Switchboard corpus show that this method results in better compression results than the conventionally used technique.