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.
|Number of pages||4|
|State||Published - 2005|
|Event||9th European Conference on Speech Communication and Technology - Lisbon, Portugal|
Duration: 4 Sep 2005 → 8 Sep 2005
|Conference||9th European Conference on Speech Communication and Technology|
|Period||4/09/05 → 8/09/05|