Enhanced Fusion Methods for Speaker Verification


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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@inproceedings{Solewicz2004EnhancedFM, title={Enhanced Fusion Methods for Speaker Verification}, author={Yosef A. Solewicz and Moshe Koppel}, year={2004} }