Score-Aging Calibration for Speaker Verification

The gradual changes that occur in the human voice due to aging create challenges for speaker verification. This study presents an approach to calibrating the output scores of a speaker verification system using the time interval between comparison samples as additional information. Several functions are proposed for the incorporation of this time information, which is viewed as aging information, in a conventional linear score calibration transformation. Experiments are presented on data with short-term aging intervals ranging between 2 months and 3 years, and long-term aging intervals of up to 30 years. The aging calibration proposal is shown to offset the decreased discrimination and calibration performance for both short-and long-term intervals, and to extrapolate well to unseen aging intervals. Relative reductions in C-llr (cost of log-likelihood ratio) of 1-4% and 10-43% are obtained at short-and long-term intervals, respectively. Assuming that a system has knowledge of the time interval between samples under comparison, this approach represents a straightforward means of compensating for the detrimental impact of aging on speaker verification performance.