Automatically correcting bias in speaker recognition systems
Yosef Solewicz and Moshe Koppel
In: 16th IEEE Workshop on Machine Learning for Signal Processing (MLSP’2006), 6-8 Sept 2006, Maynooth, Ireland.
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.