An interpretation framework for the evaluation of evidence in forensic automatic speaker recognition with limited suspect data.
Filippo Botti, Anil Alexander, Andrzej Drygajlo · 2004
This paper proposes an interpretation framework for the evaluation of evidence in forensic automatic speaker recognition where only a single recording of the suspect and a single trace (questioned recording) are provided. In such a case the withinsource variability of the suspect cannot be evaluated. An estimation of within-source and between-sources variability is performed for the case using a database of speakers recorded in the similar conditions of the case. Two measures of interpreting the evidence within the Bayesian framework are compared, one using the Likelihood Ratio (LR) and the other, giving complementary information, using Error Ratio (ER). Experimental results using the proposed methodology are presented and the two interpretation measures are discussed.