Optimal splitting of HMM Gaussian mixture components with MMIE training

A novel approach to splitting Gaussian mixture components based on the use of maximum mutual information estimation (MMIE) training is proposed. The idea is to increase acoustic resolution only in those distributions where discrimination problems are identified. Problem mixture components are determined by looking at each mixture weight counter; a large positive counter value indicates both that the component often tends not to be recognized correctly (i.e., is not part of the best path when it should be) and that there is sufficient training data to split the component. Results in a, connected digit recognition experiment on the TIDIGITS corpus indicate that much better results can be obtained with such MMIE trained digit models than with MLE trained models that use several times more mixture components.