1. In a speech recognition system using a method for recognizing human speech, the method being of the type comprising the steps of: selecting a model to represent a selected subunit of speech, the model having associated with it a plurality of states and each state having associated with it a probability function, the probability function having undetermined parameters, the probability functions being represented by a mixture of simple probability functions; extracting features from a set of speech training data; using the features to determine parameters for the probability functions in the model, an improved method for recognizing speech, the improvement comprising the steps of: determining states that may be represented by a set of simple probability functions; clustering said states that may be represented by a set of simple probability functions into a limited number of clusters; defining a plurality of cluster codebooks, one cluster codebook associated with each one of said clusters and said cluster codebooks having a large number of simple probability functions per code book; and estimating the simple probability functions in each cluster codebook to fit states in that cluster.