PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Using Post-Classifiers to Enhance Fusion of Low and High-Level Speaker Recognition
Yosef Solewicz and Moshe Koppel
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING Volume VOL. 15, Number NO. 7, pp. 2063-2071, 2007.

Abstract

This paper proposes a method for automatic correction of bias in speaker recognition systems, especially fusion-based systems. The method is based on a post-classifier which learns the relative performance obtained by the constituent systems in key trials, given the training and testing conditions in which they occurred.These conditions generally reflect train/test mismatch in factors such as channel, noise, speaker stress, etc. Results obtainedwith several state-of-the-art systems showed up to 20% decrease in EER compared to ordinary fusion in the NIST’05 Speaker Recognition Evaluation.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Speech
ID Code:3393
Deposited By:Yosef Solewicz
Deposited On:10 February 2008