Discrete MMI probability models for HMM speech recognition

This paper presents a method of non-parametrically modeling HMM output probabilities. Discrete output probabilities are estimated from a tree-based maximum mutual information (MMI) partition of the feature space, rather than the usual vector quantization. One advantage of a decision-tree method is that very high-dimensional spaces can be partitioned. Time variation can then be explicitly modeled by concatenating time-adjacent vectors, which is shown to improve recognition performance. Though the model is discrete, it provides recognition performance better than i-component Gaussian mixture HMMs on the ARPA Resource Management (RM) task. This method is not without drawbacks: because of its non-parametric nature, a large number of parameters are needed for a good model and the available RM training data is probably not sufficient. Besides the computational advantages of a discrete model, this method has promising applications in talker identification, adaptation, and clustering.