A discriminative training algorithm for predictive neural network models

Abstract

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. Those models, however, suffer from poor discrimination between acoustically similar speech signals. In this paper, we propose a new discriminative training algorithm for predictive neural network models based on the generalized probabilistic descent (GPD) algorithm and the minimum classification error formulation. The proposed algorithm allows direct minimization of a recognition error rate. Evaluation of our training algorithm on Korean digits shows its effectiveness by 30% reduction of recognition error. >