Lattice segmentation and minimum Bayes risk discriminative training for large vocabulary continuous speech recognition
Lattice segmentation techniques developed for Minimum Bayes Risk decoding in large vocabulary speech recognition tasks are used to compute the statistics needed for discriminative training algorithms that estimate HMM parameters so as to reduce the overall risk over the training data. New estimation procedures are developed and evaluated for both small and large vocabulary recognition tasks, and additive performance improvements are shown relative to maximum mutual information estimation. These relative gains are explained through a detailed analysis of individual word recognition errors. (c) 2005 Elsevier B.V. All rights reserved.
lattice segmentation discriminative training minimum bayes risk decoding large vocabulary speech recognition maximum mutual information (mmi) estimation acoustic modeling risk minimization