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Out of a hundred trials, how many errors does your speaker verifier make? Forthe user this is an important, practical question, but researchers and vendorstypically sidestep it and supply instead the conditional error-rates that aregiven by the ROC/DET curve. We posit that the user's question is answered bythe Bayes error-rate. We present a tutorial to show how to compute theerror-rate that results when making Bayes decisions with calibrated likelihoodratios, supplied by the verifier, and an hypothesis prior, supplied by theuser. For perfect calibration, the Bayes error-rate is upper bounded bymin(EER,P,1-P), where EER is the equal-error-rate and P, 1-P are the priorprobabilities of the competing hypotheses. The EER represents the accuracy ofthe verifier, while min(P,1-P) represents the hardness of the classificationproblem. We further show how the Bayes error-rate can be computed also fornon-perfect calibration and how to generalize from error-rate to expected cost.We offer some criticism of decisions made by direct score thresholding.Finally, we demonstrate by analyzing error-rates of the recently publishedDCA-PLDA speaker verifier.