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