In this paper we investigate the search effort for large vocabulary continuous speech recognition. In particular, we study the effect of different pruning techniques on the search effort and on search errors. The experimental results show that it is much more efficient in the search procedure to use a tree lexicon than a linear lexicon. For the tree search method, we study the search space in detail. For the 20 000-word task under consideration, a reasonable compromise between the search effort and the recognition accuracy can be achieved by an average number of 13 000 state hypotheses per time frame. This effort is five orders of magnitude lower than the potential size of the search space. All experiments are based on our phoneme-based large vocabulary speech recognition system used in the 1994 ARPA benchmark test.
Bibliographic reference. Ortmanns, S. / Ney, Hermann (1995): "Experimental analysis of the search space for 20 000-word speech recognition", In EUROSPEECH-1995, 901-904.