The goal of this paper is to review automatic systems for forensic speaker recognition (FSR) based on scientifically approved methods for calculation and interpretation of biometric evidence. The objective of this paper is not to promote one speaker recognition method against another, but is to make available to the biometric research community data-driven methodology combining automatic speaker recognition techniques and a rigorous forensic experimental background. Forensic speaker recognition is the process of determining if a specific individual (suspected speaker) is the source of a questioned speech recording (trace). This paper aims at reviewing forensic automatic speaker recognition (FASR) methods that provide a coherent way of quantifying and presenting recorded speech 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. Forensic speaker recognition has proven an effective tool in the fight against crime, yet there is a constant need for more research due to the difficulties involved because of the within-speaker (within-source) variability, between-speakers (between-sources) variability, and differences in recording sessions conditions.
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