Dong Yu, Li Deng, Xiaodong He, and Alex Acero
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.
In Proc. of the Interspeech Conference
Publisher International Speech Communication Association
© 2007 ISCA. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the ISCA and/or the author.