Discriminative Mixture Weight Estimation For Large Gaussian Mixture Models

This paper describes a new approach to acoustic modeling for large vocabulary continuous speech recognition (LVCSR) systems. Each phone is modeled with a large Gaussian mixture model (GMM) whose context-dependent mixture weights are estimated with a sentence-level discriminative training criterion. The estimation problem is casted in a neural network framework, which enables the incorporation of the appropriate constraints on the mixture weight vectors, and allows a straight-forward training procedure, based on steepest descent. Experiments conducted on the Callhome-English and Switchboard databases show a significant improvement of the acoustic model performance, and a somewhat lesser improvement with the combined acoustic and language models. 1. INTRODUCTION Many factors contribute to the relatively high error rates observed in LVCSR systems (e.g. diversity of speaking styles, pronunciation variants, variable degrees of articulation, noises, channel effects). By enlarging the set ..