Natural statistical models for automatic speech recognition
The performance of state-of-the-art speech recognition systems is still far worse than that of humans. This is partly caused by the use of poor statistical models. In a general statistical pattern classification task, the probabilistic models should represent the statistical structure unique to and distinguishing those objects to be classified. In many cases, however, model families are selected without verification of their ability to represent vital discriminative properties. For example, Hidden Markov Models (HMMs) are frequently used in automatic speech recognition systems even though they possess conditional independence properties that might cause inaccuracies when modeling and classifying speech signals. 
In this work, a new method for automatic speech recognition is developed where the natural statistical properties of speech are used to determine the probabilistic model. Starting from an HMM, new models are created by adding dependencies only if they are not already well captured by the HMM, and only if they increase the model's ability to distinguish one object from another. Based on conditional mutual information, a new measure is developed and used for dependency selection. If dependencies are selected to maximize this measure, then the class posterior probability is better approximated leading to a lower Bayes classification error. The method can be seen as a general discriminative structure-learning procedure for Bayesian networks. In a large-vocabulary isolated-word speech recognition task, test results have shown that the new models can result in an appreciable word-error reduction relative to comparable HMM systems.