The results suggests that score skewness has a substantial effect on system reliability. Using the Bayesian model, the Cllr ranges are 0.31 and 0.60 for i-vector and GMM-UBM systems respectively when scores are skewed, and the Cllr range remains stable when scores follow a normal distribution irrespective of sample size. When scores follow a normal distribution, Cllr ranges reduce to 0.49 (i-vector) and 0.69 (GMM-UBM). Using logistic regression with small samples of skewed scores, Cllr range is 1.3 for the i-vector system and 0.69 for the GMM-UBM system. In the present study, simulated scores were generated from i-vector and GMM-UBM automatic speaker recognition systems using real speech data to demonstrate the variability in system reliability as a function of score skewness, sample size, and calibration methods (logistic regression or a Bayesian model). Numerous studies have focused on improving system validity, while studies of reliability are comparatively limited. In data-driven forensic voice comparison (FVC), empirical testing of a system is an essential step to demonstrate validity and reliability.
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