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This paper presents a fast discriminative training algorithm for sequences of observations. It considers a sequence of feature vectors as one single composite token in training or testing. In contrast to the traditional EM algorithm, this algorithm is derived from a discriminative objective, aiming at directly minimizing the recognition error. Compared to the gradient-descent algorithms for discriminative training, this algorithm invokes a mild assumption which leads to closed-form formulas fordoi:10.1109/icassp.2003.1202333 dblp:conf/icassp/LiJ03 fatcat:rdxa6nxpobew5fgde7krmgsauy