Minimum classification error training of landmark models for real-time continuous speech recognition

Abstract

Though many studies have shown the effectiveness of the minimum classification error (MCE) approach to discriminative training of HMM for speech recognition, few if any have reported MCE results for large (> 100 hours) training sets in the context of real-world, continuous speech recognition. Here we report large gains in performance for the MIT JUPITER weather information task as a result of MCE-based batch optimization of acoustic models. Investigation of word error rate versus computation time showed that small MCE models significantly outperform the maximum likelihood (ML) baseline at all points of equal computation time, resulting in up to 20% word error rate reduction for in-vocabulary utterances. The overall MCE loss function was minimized using Quickprop, a simple but effective second-order optimization method suited to parallelization over large training sets.