Exploiting correlations among competing models with application to large vocabulary speech recognition

In a typical speech recognition system, computing the match between an incoming acoustic string and many competing models is computationally expensive. Once the highest ranking models are identified, all other match scores are discarded. The authors propose to make use of all computed scores by means of statistical inference. They view the match between an incoming acoustic string s and a model M_i as a random variable Y_i. The class-conditioning distributions of (Y ₁,. . .Y_N) can be studied offline by sampling, and then used in a variety of ways. For example, the means of these distributions give rise to a natural measure of distance between models. One of the most useful applications of these distributions is as a basis for a new Bayesian classifier. The latter can be used to significantly reduce search effort in large vocabularies, and to quickly obtain a short list of candidate words. An example hidden Markov model (HMM)-based system shows promising results