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2004 Technical Reports

Discriminative Training for Large Margin HMMs

Hui Jiang

Technical Report CS-2004-01

York University

March 30, 2004


In this report, motivated by large margin classifiers in machine learning, we propose a new discriminative training criterion for estimating CDHMM (continuous density hidden Markov model) in speech recognition based on the principle of maximizing the minimum multi-class separation margin. In this report, we first show that this maximum margin model estimation problem can be formulated as a standard constrained minimax optimization problem. Alternatively, we also show that the estimation problem can be solved by a GPD (generalized probabilistic descent) algorithm if we approximate the objective function by a continuous and differentiable function, such as summation of exponential functions. In this report, we also propose a method to handle classification errors in training set in maximum margin estimation by using them to optimize a separate objective function which is similar to that in the MCE (minimum classification error) formulation.

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