Modeling intra-speaker variability for speaker recognition

In this paper we present a speaker recognition algorithm that models explicitly intra-speaker inter-session variability. Such variability may be caused by changing speaker characteristics (mood, fatigue, etc.), channel variability or noise variability. We define a session-space in which each session (either train or test session) is a vector. We then calculate a rotation of the session-space for which the estimated intra-speaker subspace is isolated and can be modeled explicitly. We evaluated our technique on the NIST-2004 speaker recognition evaluation corpus, and compared it to a GMM baseline system. Results indicate significant reduction in error rate.