A Fast Discriminative Training Algorithm for Minimum Classification Error
نویسندگان
چکیده
In this paper a new algorithm is proposed for fast discriminative training of hidden Markov models (HMMs) based on minimum classification error (MCE). The algorithm is able to train acoustic models in a few iterations, thus overcoming the slow training speed typical of discriminative training methods based on gradient-descendent. The algorithm tries to cancel the gradient of the objective function in every iteration. Re-estimation expressions of the HMM parameters are derived. Experiments with triphone and word models show that the proposed algorithm always achieves much better results in a single iteration than MCE, MMI or MPE do over several iterations.
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Discriminative methods are used for increasing pattern recognition and classification accuracy. These methods can be used as discriminant transformations applied to features or they can be used as discriminative learning algorithms for the classifiers. Usually, discriminative transformations criteria are different from the criteria of discriminant classifiers training or their error. In this ...
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