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We present an efficient technique of generating word graphs in a continuous speech recognition system. The word graph is constructed in two stages. In the first stage, a huge word graph is generated as a by-product of a beam-driven forward search. Based on a dynamic-programming (DP) method, this huge word graph will be pruned in the second stage using higher level knowledge, such as n-gram language models. In this pruning stage an edge is removed if there is no path going through this edge which is better scored as the best-scored path in the word graph. The proposed technique is evaluated in the German VERBMOBIL task.
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Kühn et al. (2002) studied this question.
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