Very large vocabulary isolated utterance recognition: a comparison between one pass and two pass strategies

A system for recognizing isolated utterances belonging to a very large vocabulary is presented that follows a two-pass strategy. The first step, hypothesization, consists in the selection of a subset of word candidates, starting from the segmentation of speech into six broad phonetic classes. This module is implemented through a dynamic programming algorithm working in a three-dimensional space. The search is performed on a tree representing a coarse description of the lexicon. The second step is the search for the best N candidates according to a maximum-likelihood criterion. Each word candidate is represented by a graph of subword hidden Markov models, and a tree structure of the whole word subset is built on line for an efficient implementation of the Viterbi algorithm. A comparison with a direct approach that does not use the hypothesization module shows that the two-pass approach has the same performance with an 80% reduction in computational complexity.