Study of a fast discriminative training algorithm for pattern recognition.

Discriminative training refers to an approach to pattern recognition based on direct minimization of a cost function commensurate with the performance of the recognition system. This is in contrast to the procedure of probability distribution estimation as conventionally required in Bayes' formulation of the statistical pattern recognition problem. Currently, most discriminative training algorithms for nonlinear classifier designs are based on gradient-descent (GD) methods for cost minimization. These algorithms are easy to derive and effective in practice, but are slow in training speed and have difficulty selecting the learning rates. To address the problem, we present our study on a fast discriminative training algorithm. The algorithm initializes the parameters by the expectation-maximization (EM) algorithm, and then uses a set of closed-form formulas derived in this paper to further optimize a proposed objective of minimizing error rate. Experiments in speech applications show that the algorithm provides better recognition accuracy in a fewer iterations than the EM algorithm and a neural network trained by hundreds of GD iterations. Although some convergent properties need further research, the proposed objective and derived formulas can benefit further study of the problem.