Channel Factors Compensation in Model and Feature Domain for Speaker Recognition

The variability of the channel and environment is one of the most important factors affecting the performance of text-independent speaker verification systems. The best techniques for channel compensation are model based. Most of them have been proposed for Gaussian Mixture Models, while in the feature domain typically blind channel compensation is performed. The aim of this work is to explore techniques that allow more accurate channel compensation in the domain of the features. Compensating the features rather than the models has the advantage that the transformed parameters can be used with models of different nature and complexity, and also for different tasks. In this paper we evaluate the effects of the compensation of the channel variability obtained by means of the channel factors approach. In particular, we compare channel variability modeling in the usual Gaussian Mixture model domain, and our proposed feature domain compensation technique. We show that the two approaches lead to similar results on the NIST 2005 Speaker Recognition Evaluation data. Moreover, the quality of the transformed features is also assessed in the Support Vector Machines framework for speaker recognition on the same data, and in preliminary experiments on Language Identification.