A fast lexical selection strategy for large vocabulary continuous speech recognition

The paper describes an algorithm for performing fast selection of word candidates to speed up the decoding of continuous speech. Interacting with word transitions, it fits very well into standard Viterbi decoding. The main features are: a tree-organization of the lexicon, coarse models deduced from the detailed ones, a selection rule compatible with unknown word boundaries, an efficient dynamic programming implementation. Various applications show a global reduction of the search cost by a factor 5 to 10 depending on the lexicon size, the error rate being increased by less than 1%.