Modeling Intra-Speaker Variability for Speaker Recognition
Hagai Aronowitz, Dror Irony and David Burstein
In: INTERSPEECH 2005, 4-8 Sep 2005, Lisbon, Portugal.

Abstract

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.

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