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

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.

2003-chelba-eurospeech.pdf
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In  Proc. of the Eurospeech Conference

Publisher  International Speech Communication Association
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