Experimental results on large vocabulary continuous speech understanding

System performance is presented for a continuous speech understanding system. The system accepts natural-language queries, with a lexicon of 1000 words. Recognition and understanding work in cascade, a speaker-dependent hidden Markov model (HMM)-based word hypothesizer, generates a lattice of word candidates that is given to a grammar-based understanding stage that extracts the correct word sequence together with its meaning. The authors focus on recent improvements added at the understanding level, consisting of a new statistical modeling of correct word adjacency and function-word losses. Such modeling permits the main problems of interword coarticulation to be dealt with and produces substantial advances in terms of recognition accuracy at a low computational cost. System performance with a language model of perplexity 25 results in 87% correct sentence understanding