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 norion of phonetic classes to decompose the dictionnary into sub-dictionnaries. We develop a two-stage recognirion algorithm: - Each dicrionnary which is considered as a sequence of phonetics classes is modeled by a HMM where the elementaries units are these phonerics classes. - Each word is modeled by a classical HMM where the elementary unit is the pseudodyphone. For a unknown word utterance, a fust 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. Experimenu are carried out with telephonic database
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