PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:1688
Deposited By:Hagai Aronowitz
Deposited On:28 November 2005