Discriminative training of n-gram classifiers for speech and text routing
Ciprian I. Chelba, Alex Acero · 2003
We present a method for conditional maximum likelihood esti-mation of N-gram models used for text or speech utterance clas-sification. The method employs a well known technique relying on a generalization of the Baum-Eagon inequality from poly-nomials 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. 1.