Automatically Correcting Bias in Speaker Recognition Systems

In this paper we present a general machine learning framework for score bias reduction and analysis in speaker recognition systems. The general principle is to learn a meta-system using recognition systems´ errors, given the training and testing conditions in which they occurred. In the context of speaker recognition, the proposed method is able to reduce the bias introduced in scores due to a variety of factors such as channel mismatch, additive noise, gender mismatch, different speaking styles, etc. Moreover, this framework enables a deep understanding of the origins of score bias in any system, which will support an optimized system redesign. Preliminary results obtained with several state-of-the-art systems showed considerable improvement in original performance, in addition to identifying sources of system bias.