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Increasing the generalization capability of Discriminative Training (DT) of Hidden Markov Models (HMM) has recently gained an increased interest within the speech recognition field. In particular, achieving such increases with only minor modifications to the existing DT method is of significant practical importance. In this paper, we propose a solution for increasing the generalization capability of a widely-used training method -the Minimum Classification Error (MCE) training of HMM -withdoi:10.1109/icassp.2010.5495109 dblp:conf/icassp/LiM10a fatcat:cbcaqmi4kndqjdtlqzkxsbxcua