L. Ferrer, M. Graciarena, A. Zymnis, and E. Shriberg, “System combination using auxiliary information for speaker verification,” in Proc. 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4853–4857.
Abstract
Recent studies in speaker recognition have shown that score-level 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.
Index Terms— Speaker recognition, System combination, Auxiliary Information, Nonnative speech, Logistic Regression