PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Enhanced fusion methods for speaker verification
Yosef Solewicz and Moshe Koppel
In: 9th International Conference Speech and Computer, 20-22 Sep 2004, Saint-Petersburg, Russia.

Abstract

This paper presents meta-learning schemes aimed at improving fusion of low and high level information for speaker verification in clean and noisy environments. While traditional systems fuse several classifier outputs in a uniform fashion independently of test quality, the proposed schemes use selective fusion weights according to test quality. A decrease of more than 20% under noisy conditions and 10% under clean conditions could be obtained with little calibration.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Speech
ID Code:794
Deposited By:Yosef Solewicz
Deposited On:30 December 2004