Experimental results on a large lexicon access task

In this paper the problem of lexical access to large vocabularies by means of a coarse phonetic description of words is addressed. A generate and test approach is used: first a set of word candidates is extracted from the lexicon by means of a broad phonetic description of the input utterance, then a more detailed stochastic model of each word in this set, based on sub-word phonetic units, is obtained, and the likelihood of the candidate words is estimated using the Viterbi algorithm. Results of the application of the method to a large vocabulary isolated word recognition task are given. The candidate lists produced in the generation phase include the correct word in 98 times out of 100, their average size is of the order of 50 items for a 1011 word lexicon, while they do not exceed 300 units for a 13748 word lexicon.