An efficient two-pass search algorithm using word trellis index

Abstract

We propose an e cient two-pass search algorithm for LVCSR. Instead of conventional word graph, the rst preliminary pass generates \word trellis index, keeping track of all survived word hypotheses within the beam every time-frame. As it represents all found word boundaries non-deterministically, we can (1) obtain accurate sentence-dependent hypotheses on the second search, and (2) avoid expensive word-pair approximation on the rst pass. The second pass performs an e cient stack decoding search, where the index is referred to as predicted word list and heuristics. Experimental results on 5,000-word Japanese dictation task show that, compared with the word-graph method, this trellis-based method runs with less than 1/10 memory cost while keeping high accuracy. Finally, by handling inter-word context dependency, we achieved the word error rate of 5.6%.