Open AccessOn‐Line Linear Combination of Classifiers Based on Incremental Information in Speaker Verification
Author(s) -
Huenupán Fernando,
Yoma Néstor Becerra,
Garretón Claudio,
Molina Carlos
Publication year - 2010
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.10.0109.0301
Subject(s) - a priori and a posteriori , classifier (uml) , computer science , pattern recognition (psychology) , artificial intelligence , word error rate , speaker verification , random subspace method , machine learning , data mining , speaker recognition , epistemology , philosophy
A novel multiclassifier system (MCS) strategy is proposed and applied to a text‐dependent speaker verification task. The presented scheme optimizes the linear combination of classifiers on an on‐line basis. In contrast to ordinary MCS approaches, neither a priori distributions nor pre‐tuned parameters are required. The idea is to improve the most accurate classifier by making use of the incremental information provided by the second classifier. The on‐line multiclassifier optimization approach is applicable to any pattern recognition problem. The proposed method needs neither a priori distributions nor pre‐estimated weights, and does not make use of any consideration about training/testing matching conditions. Results with Yoho database show that the presented approach can lead to reductions in equal error rate as high as 28%, when compared with the most accurate classifier, and 11% against a standard method for the optimization of linear combination of classifiers.