Forensic identification reporting using automatic speaker recognition systems

We show how any speaker recognition system can be adapted to provide its results according to the Bayesian approach for evidence analysis and forensic reporting. This approach, firmly established in other forensic areas as fingerprint, DNA or fiber analysis, suits the needs of both the court and the forensic scientist. We show the inadequacy of the classical approach to forensic reporting because of the use of thresholds and the suppression of the prior probabilities related to the case. We also show how to assess the performance of those forensic systems through Tippet plots. Finally, an example is shown using NIST-Ahumada eval'2001 data, where the speaker recognition abilities of our system are assessed through DET plots, using then these raw scores as evidences into the forensic system, where relative to populations we will obtain the corresponding likelihood ratios values, which are assessed through Tippet (1968) plots.