Generation and expansion of word graphs using long span context information

An algorithm for the generation of word graphs in a cross-word decoder that uses long span m-gram language models (LMs) is presented. The generation of word hypotheses within the graph relies on the word m-tuple-based boundary optimization. The graphs contain the full word history knowledge information since the graph structure reflects all LM constraints used during the search. This results in better word boundaries and in enhanced capabilities to prune the graphs. Furthermore, the memory costs for expanding the m-gram constrained word graphs to apply very long span LMs (e.g. ten-grams that are constructed by log linear LM combination) are considerably reduced. Experiments for lattice generation and rescoring have been carried out on the 5K-word WSJ task and the 64K-word NAB task.