Statistical methods and Bayesian interpretation of evidence in forensic automatic speaker recognition

The goal of this paper is to establish a robust methodology for forensic automatic speaker recognition (FASR) based on sound statistical and probabilistic methods, and validated using databases recorded in real-life conditions. The interpretation of recorded speech as evidence in the forensic context presents particular challenges. The means proposed for dealing with them is through Bayesian inference and corpus based methodology. A probabilistic model – the odds form of Bayes’ theorem and likelihood ratio – seems to be an adequate tool for assisting forensic experts in the speaker recognition domain to interpret this evidence. In forensic speaker recognition, statistical modelling techniques are based on the distribution of various features pertaining to the suspect's speech and its comparison to the distribution of the same features in a reference population with respect to the questioned recording. In this paper, the state-of-the-art automatic, text-independent speaker recognition system, using Gaussian mixture model (GMM), is adapted to the Bayesian interpretation (BI) framework to estimate the within-source variability of the suspected speaker and the between-sources variability, given the questioned recording. This doublestatistical approach (BI-GMM) gives an adequate solution for the interpretation of the recorded speech as evidence in the judicial process.