Large-vocabulary continuous speech recognition using linear lexicon search and 1-best approximation tree-structured lexicon search

The computational cost of a large-vocabulary continuous speech recognition system based on HMM is proportional to its vocabulary size. A tree-structured lexicon is generally used to reduce the number of HMM states. An approximation of the dependence of word boundaries and likelihoods on word histories is also used to limit the increase in the number of hypotheses in the forward decoding procedure. We first compared search algorithms with a tree-structured lexicon using certain approximation methods and algorithms with a linear lexicon. The algorithm based on 1-best approximation with a tree-structured lexicon is efficient but frequently misses the optimal sentence hypothesis. Linear lexicon search can find the optimal hypothesis but has a high computational cost. Thus, we propose a search method using 1-best approximation treestructured lexicon search and linear lexicon search. The words expanded in the linear lexicon are dynamically selected according to likelihood. We evaluated this new search algorithm using read speech, broadcast news speech, and lecture speech, and obtained significant improvement of recognition performance, expanding only 250 to 500 words out of 20,000 words in the linear lexicon. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(7): 31–39, 2005; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/scj.20252