Estimating and Evaluating Confidence for Forensic Speaker Recognition

Abstract

Estimating and evaluating confidence has become a key aspect of the speaker recognition problem because of the increased use of this technology in forensic applications. We discuss evaluation measures for speaker recognition and some of their properties. We then propose a framework for confidence estimation based upon scores and meta-information, such as utterance duration, channel type, and SNR. The framework uses regression techniques with multilayer perceptrons to estimate confidence with a data-driven methodology. As an application, we show the use of the framework in a speaker comparison task drawn from the NIST 2000 evaluation. A relative comparison of different types of meta-information is given. We demonstrate that the new framework can give substantial improvements over standard distribution methods of estimating confidence.