Recognition of Words in Very Large Vocabulary

Abstract

Word pre-selection by means of partial phonetic descriptions is a method of lexical access in speech recognition systems for very large vocabularies that is being receiving particular attention. It can be effective provided that segmentation errors are taken into account within the lexical access procedure, and that the resulting candidate word set is reasonably sized. As errors in the segmentation of input utterances are unavoidable, even if a limited number of phonetic categories must be discriminated, a lattice of segmentation hypotheses is generated. Word pre-selection is obtained, therefore, by matching a lattice of phonetic hypotheses against a graph structure that represents a generic word. A Dynamic Programming procedure is introduced that solves this problem. A sub-optimal solution and heuristic constraints have been investigated that improve the algorithm efficiency. In the second step, word verification, a detailed ‘representation of the phonemic structure of word candidates is used for estimating the most likely words. Words are modeled by sequences of sub-word units represented by Hidden Markov Models and a beam-search Viterbi algorithm estimates their likelihood. Experimental results on large vocabularies demonstrate the effectiveness of the method.