A Generalized Feature Transformation Approach for Channel Robust Speaker Verification

In this paper we propose a generalized feature transformation approach to compensating for channel variation in speaker verification (SV) applications. Channel-dependent (CD) piecewise linear transformations are used for feature compensation. CD transformation parameters are estimated together with a channel-independent (CI) root Gaussian mixture model (GMM) from training data with a variety of channel conditions by using a maximum likelihood criterion. Experiments are conducted on the 2005 NIST Speaker Recognition Evaluation (SRE) corpus for several text-independent GMM-based SV systems. Experimental results show that the proposed approach achieves relative equal error rate (EER) reductions of 8.19% and 26.24% in comparison with a traditional feature mapping approach and a baseline system, respectively.