Discriminative Training of N-gram Classifiers for Speech and Text Routing

Ciprian Chelba and Alex Acero

Abstract

We present a method for conditional maximum likelihood estimation of N-gram models used for text or speech utterance classification. The method employs a well known technique relying on a generalization of the Baum-Eagon inequality from polynomials to rational functions. The best performance is achieved for the 1-gram classifier where conditional maximum likelihood training reduces the class error rate over a maximum likelihood classifier by 45% relative.

Details

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