  Prior-weighted logistic regression has become a standard tool for calibration
in speaker recognition. Logistic regression is the optimization of the expected
value of the logarithmic scoring rule. We generalize this via a parametric
family of proper scoring rules. Our theoretical analysis shows how different
members of this family induce different relative weightings over a spectrum of
applications of which the decision thresholds range from low to high. Special
attention is given to the interaction between prior weighting and proper
scoring rule parameters. Experiments on NIST SRE'12 suggest that for
applications with low false-alarm rate requirements, scoring rules tailored to
emphasize higher score thresholds may give better accuracy than logistic
regression.
