A word graph based N-best search in continuous speech recognition

Abstract

The authors introduce an efficient algorithm for the exhaustive search of N-best sentence hypotheses in a word graph. The search procedure is based on a two-pass algorithm. In the first pass, a word graph is constructed with standard time-synchronous beam search. The actual extraction of N-best word sequences from the word graph takes place during the second pass. With the implementation of a tree-organized N-best list, the search is performed directly on the resulting word graph. Therefore, the parallel bookkeeping of N hypotheses at each processing step during the search is not necessary. It is important to point out that the proposed N-best search algorithm produces an exact N-best list as defined by the word graph structure. Possible errors can only result from pruning during the construction of the word graph. In a postprocessing step, the N candidates can be rescored with a more complex language model with highly reduced computational cost. This algorithm is also applied in speech understanding to select the most likely sentence hypothesis that satisfies some additional constraints.