Speaker Verification with Confidence and Reliability Measures

In pattern recognition, the need to quantify the quality of a classifier´s output has gained importance in the past years. Speaker verification is no exception. This paper presents a probabilistic reliability framework incorporating signal-domain information into the confidence estimation and contrasts this method with classical approaches to estimating the confidence in a given speaker verification classifier output. We show that the method proposed can deal with adverse acoustic conditions for a wide range of signal-to-noise ratios, does not depend on a Gaussian assumption for impostor and client score distributions, and presents benefits in terms of scalability and interpretability of the measure. We contrast reliability and confidence approaches, and evaluate performance on a degraded version of the 295-users XM2VTS database