Value and interpretation of biometric evidence in forensic automatic speaker recognition.

Abstract

Forensic speaker recognition is the process of determining if a specific individual (suspected speaker) is the source of a questioned voice recording (trace). Forensic automatic 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 in adapting automatic methods of voice comparison to the forensic methodology that provides a coherent way of assessing and presenting recorded speech as scientific evidence. The ongoing paradigm shift in the forensic speaker recognition needs biometric methods for the calculation of the evidence value, its strength, and the evaluation of this strength under operating conditions of the casework. In such methods, the biometric evidence consists of the quantified degree of similarity between speaker-dependent features extracted from the trace and speaker-dependent features extracted from recorded speech of a suspect, represented by his/her model. This presentation aims at introducing deterministic and statistical automatic speaker recognition methods that provide several ways of quantifying and presenting recorded voice as biometric evidence, as well as the assessment of its strength (likelihood ratio) in the Bayesian interpretation framework, including scoring and direct methods, compatible with interpretations in other forensic disciplines.