Bayes risk approximations using time overlap with an application to system combination

The computation of the Minimum Bayes Risk (MBR) decoding rule for word lattices needs approximations. We investigate a class of approximations where the Levenshtein alignment is approximated under the condition that competing lattice arcs overlap in time. The approximations have their origins in MBR decoding and in discriminative training. We develop modified versions and propose a new, conceptually extremely simple confusion network algorithm. The MBR decoding rule is extended to scope with several lattices, which enables us to apply all the investigated approximations to system combination. All approximations are tested on a Mandarin and on an English LVCSR task for a single system and for system combination. The new methods are competitive in error rate and show some advantages over the standard approaches to MBR decoding.