Use of incrementally regulated discriminative margins in MCE training for speech recognition

In this paper, we report our recent development of a novel discriminative learning technique which embeds the concept of discriminative margin into the well established minimum classification error (MCE) method. The idea is to impose an incrementally adjusted “margin ” in the loss function of MCE algorithm so that not only error rates are minimized but also discrimination “robustness ” between training and test sets is maintained. Experimental evaluation shows that the use of the margin improves a state-of-the-art MCE method by reducing 17 % digit errors and 19 % string errors in the TIDigits recognition task. The string error rate of 0.55 % and digit error rate of 0.19 % we have obtained are the best-ever results reported on this task in the literature. Index Terms: discriminative training, margin, minimum error 1.