Reconnaissance de la parole dans le cadre de très grands vocabulaires

Abstract
This paper describes a new strategy for very large vocabulary speech recognition. The main problem is to reduce the lexical access without pruning the correct candidate. We propose to exploit the branching structure of BDLEX and the description of each word into root and flexional ending. More we use the notion of phonetic classes to decompose the dictionnary into sub-dictionnaries. We develop a two-stage recognition algorithm : - Each dictionnary which is considered as a sequence of phonetics classes is modeled by a HMM where the elementaries units are these phonetics classes. - Each word is modeled by a classical HMM where the elementary unit is the pseudodyphone. For a unknown word utterance, a first recognition gives the best dictionnary to which it belongs, the Viterbi algorithm applied to the network of the best dictionnary words, gives the word with the most likelihood. Experiments are carried out with telephonic database.