Scoring and direct methods for the interpretation of evidence in forensic speaker recognition

In forensic speaker recognition, the strength of evidence is estimated using the likelihood ratio, which is the relative probability of observing the evidence, given the hypothesis that the suspect is the source of the questioned recording and the hypothesis that anyone else in a relevant potential population is its source. In order to calculate the likelihood ratio we use two approaches; one, directly using the likelihoods returned by the Gaussian Mixture Models (GMMs), and the other by modeling the distributions of these likelihood scores and then deriving the likelihood ratio on the basis of these score distributions. The former approach is used implicitly in speaker verification systems, although in forensic speaker recognition, the latter is preferred as it does not depend on the automatic speaker recognition technique used. However, both these methods have their advantages and disadvantages. In this paper, we propose statistical representations in order to evaluate the strength of evidence in each of these two methods.