Intra-session variability compensation and a hypothesis generation and selection strategy for speaker segmentation

This paper addresses the problem of speaker segmentation in two-speaker telephone conversations, using an eigenvoice based factor analysis approach. We present a set of improvements in the speaker segmentation system. First, we study two methods to compensate for intra-session variability, that is the variability present in a speaker during a single session. Secondly we propose a method to generate segmentation hypotheses that combined with a given confidence measure, enables the selection of correct hypotheses improving the overall segmentation performance. The proposed improvements are evaluated on the NIST Speaker Recognition Evaluation 2008 summed channel test condition, obtaining 28% relative improvement in terms of speaker segmentation error.