System combination using auxiliary information for speaker verification

Recent studies in speaker recognition have shown that scorelevel combination of subsystems can yield significant performance gains over individual subsystems. We explore the use of auxiliary information to aid the combination procedure. We propose a modified linear logistic regression procedure that conditions combination weights on the auxiliary information. A regularization procedure is used to control the complexity of the extended model. Several auxiliary features are explored. Results are presented for data from the 2006 NIST speaker recognition evaluation (SRE). When an estimated degree of nonnativeness for the speaker is used as auxiliary information, the proposed combination results in a 15% relative reduction in equal error rate over methods based on standard linear logistic regression, support vector machines, and neural networks.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Luciana Ferrer, Martin Graciarena, Argyrios Zymnis, Elizabeth Shriberg
• Keywords: Auxiliary Information, ICASSP 2008, Linear Logistic Regression, Signal Processing, Speaker Recognition