AN INVESTIGATION OF FRAME DISCRIMINATION FOR CONTINUOUS SPEECH RECOGNITION
D. Povey and P.C. Woodland
This report describes the implementation of a discriminative HMM parameter estimation technique known as Frame Discrimination (FD) for large vocabulary speech recognition, and reports improvements in accuracy over ML-trained and MMI-trained models. Features of the implementation include the use of an algorithm called the Roadmap algorithm which selects the most important Gaussians for a given input frame without calculating every Gaussian probability in the system, a new distance measure between Gaussians based on overlap (which is used in the Roadmap algorithm), and an investigation of improvements to the Extended Baum-Welch formulae. Frame Discrimination estimation is found to give error rates at least as good as MMI with considerably less computational effort.
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