Paper Info
Title
DETAC: a discriminative criterion for speaker verification
Abstract
This paper introduces a general criterion applicable to dis- criminative training of detection systems, and discusses its particular implementation in GMM-based text-independent speaker veriflcation. Based on an analysis of the detection error trade-ofi curve of a baseline system, we argue that the new criterion extends several conventional methods such as the maximum posterior training by logistic regression and the linear discriminative analysis projection, by a second aspect - \reshaping" the Bayes error area in favor of a rel- evant operating range. Optimization results with relative error reduction of up to 16% are presented on the cellular task of the NIST-2001 speaker recognition evaluation.
Year
Venue
Keywords
2002
INTERSPEECH
logistic regression,relative error
Field
DocType
Citations 
Speaker verification,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Discriminative model
Conference
6
PageRank 
References 
Authors
0.98
5
2
Name
Order
Citations
PageRank
Jiri Navratil131431.36
Ganesh N. Ramaswamy221325.72