Annotating large lattices with the exact word error
Rogier C. van Dalen, Mark Gales · 2015
The acoustic model in modern speech recognisers is trained discriminatively, for example with the minimum Bayes risk. This criterion is hard to compute exactly, so that it is normally approximated by a criterion that uses fixed alignments of lat-tice arcs. This approximation becomes particularly problematic with new types of acoustic models that require flexible align-ments. It would be best to annotate lattices with the risk mea-sure of interest, the exact word error. However, the algorithm for this uses finite-state automaton determinisation, which has exponential complexity and runs out of memory for large lat-tices. This paper introduces a novel method for determinis-ing and minimising finite-state automata incrementally. Since it uses less memory, it can be applied to larger lattices. Index Terms: speech recognition, discriminative training, min-imum Bayes risk