Compressing the Factoring Table and Performing Garbage Collection on Unusable Word Hypotheses in a Continuous Speech Recognition System

摘要

We have investigated methods for reducing the number of word hypotheses registered in the word graph and the amount of memory used by the factoring tables for the tree-structured dictionary with the objective of reducing the memory requirements of a continuous speech recognition system. By assigning word hypotheses in the word graph attributes relating to the number of continuation hypotheses in which they are included, we are able to efficiently determine unusable word hypotheses during pruning and can perform garbage collection. This procedure allows us to reduce the amount of memory needed for generating word hypotheses from 127 MB to 6.9 MB. In addition, by approximating the bigram values held in the factoring tables with POS bigrams, we were able to reduce the memory consumption of the factoring tables from 56 MB to 19 MB with almost no impairment of recognition performance. As a result of these reductions in memory requirements, the memory consumption of the decoder has been reduced from 246 MB to 113 MB.