This monograph describes an experiment in Forensic Speaker Identification, showing how speech samples from the same speaker can be discriminated from speech from different speakers with acoustic features commonly used in forensics. It also explains what is now considered the legally and logically correct approach to Forensic Speaker Identification, and presents data that can be used both in real casework and in further testing. Forensic Speaker Identification is typically concerned with addressing the question of whether two or more speech samples have been produced by the same, or different, speakers. It is clear from recent research that the legally and logically correct way of doing this is by using a Bayesian Likelihood Ratio. The monograph explains what a Likelihood Ratio is; why its use is now considered correct; and how it can be used to successfully discriminate same-speaker pairs from different-speaker pairs. The monograph shows how the Likelihood Ratio is a ratio of the probability of the evidence given a hypothesis (e.g. that the two samples are from the same speaker) to the probability of the evidence given a competing hypothesis (e.g. that the speech samples are from different speakers). This can be seen as a ratio expressing the similarity of the samples, divided by the typicality of the samples (i.e. how common these similarities are in the rest of the population). Since same-subject pairs are predicted by theory to have Likelihood Ratios greater than unity, and different-subject pairs are predicted to have Likelihood Ratios smaller, the Likelihood Ratio lends itself to use as a discriminant function to discriminate same-speaker from different-speaker speech samples. The extent to which this is possible is vital knowledge, given the legal evidentiary standards now accepted in the wake of the well-known Daubert rulings.
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