Ciprian Chelba and Alex Acero
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.
|Published in||Proc. of the Eurospeech 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.