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We describe a novel approach to Bayes risk(BR) decoding for speech recognition,in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error(MWE) metric.To achieve this,we propose improved forward and backward algorithms on the lattices and the whole procedure is optimized recursively.The remarkable characteristics of the proposed approach are that the optimization procedure is expectation-maximization(EM) like and the formation of the updated result is similar to that obtained with the confusion network(CN) decoding method.Experimental results indicated that the proposed method leads to an error reduction for both lattice rescoring and lattice-based system combinations,compared with CN decoding,confusion network combination(CNC),and ROVER methods.
We describe a novel approach to Bayes risk (BR) decoding for speech recognition, in which we we try to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error (MWE) metric. To achieve this, we propose improved forward and backward algorithms on the lattices and the whole procedure is optimized recursively. The remarkable characteristics of the proposed approach are that the optimization procedure is expectation-maximization (EM) like and the formation of the updated result is similar to that obtained with the confusion network (CN) decoding method. Experimental results indicated that the proposed method leads to an error reduction for both lattice rescoring and lattice-based system combinations, compared with CN decoding, confusion network combination (CNC), and ROVER methods.