Voice: Biometric Analysis and Interpretation of

Forensic speaker recognition (FSR) is the process of determining if a specific individual (suspected speaker) could be the source of a questioned voice recording (trace). Results of FSR-based investigations may be of pivotal importance at any stage of an inquiry. The expert's role is to testify to the worth of the voice evidence by using, if possible, a quantitative measure of this worth. This article aims at presenting advances in European research on 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 in the Bayesian interpretation framework, compatible with interpretations in other forensic disciplines. FSR has proved an effective tool, yet there is a constant need for more research because of the difficulties involved in real-life environment (within-speaker variability, between-speaker variability, ambient noise, other sounds, transmission channels, and mismatched recording conditions).