Automatic Speaker Recognition for Forensic Case Assessment and Interpretation

Abstract

Forensic speaker recognition (FSR) is the process of determining if a specific individual (suspected speaker) is the source of a questioned voice recording (trace). The forensic expert’s role is to testify to the worth of the voice evidence by using, if possible, a quantitative measure of this worth. It is up to the judge and/or the jury to use this information as an aid to their deliberations and decision. This chapter aims at presenting research advances in forensic automatic speaker recognition (FASR), including data-driven tools and related methodology, that provide a coherent way of quantifying and presenting recorded voice as biometric evidence , as well as the assessment of its strength (likelihood ratio) in the Bayesian interpretation framework, compatible with interpretations in other forensic disciplines. Step-by-step guidelines for the calculation of the biometric evidence and its strength under operating conditions of the casework are provided in this chapter. It also reports on the European Network of Forensic Science Institutes (ENFSI) evaluation campaign through a fake (simulated) case, organized by the Netherlands Forensic Institute (NFI), as an example, where an automatic method using the Gaussian mixture models (GMMs) and the Bayesian interpretation (BI) framework were implemented for the forensic speaker recognition task.