High quality word graphs using forward-backward pruning

This paper presents an efficient method for constructing high quality word graphs for large vocabulary continuous speech recognition. The word graphs are constructed in a two-pass strategy. In the first pass, a huge word graph is produced using the time-synchronous lexical tree search method. Then, in the second pass, this huge word graph is pruned by applying a modified forward-backward algorithm. To analyze the characteristic properties of this word graph pruning method, we present a detailed comparison with the conventional time-synchronous forward pruning. The recognition experiments, carried out on the North American Business (NAB) 20000-word task, demonstrate that, in comparison to the forward pruning, the new method leads to a significant reduction in the size of the word graph without an increase in the graph word error rate