Use of Incrementally Regulated Discriminative Margins in MCE Training for Speech Recognition

Dong Yu, Li Deng, Xiaodong He, and Alex Acero

Abstract

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.

Details

Publication typeInproceedings
Published inProc. of the Interspeech Conference
PublisherInternational Speech Communication Association
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