The Effect of the Within-Speaker Sample Size on the Performance of Likelihood Ratio Based Forensic Voice Comparison: Monte Carlo Simulations

This study is an investigation into the effect of sample size on a likelihood ratio (LR) based forensic voice comparison (FVC) system. In particular, we looked into how the offender and suspect sample size (or the within-speaker sample size) would affect the performance of the FVC system, using spectral feature vectors extracted from spontaneous Japanese speech. For this purpose, we repeatedly conducted Monte Carlo method based experiments with different sample size, using the statistics obtained from these feature vectors. LRs were estimated using the multivariate kernel density LR formula developed by Aitken and Lucy (2004). The derived LRs were calibrated using the logistic-regression calibration technique proposed by Brümmer and du Preez (2006). The performance of the FVC system was assessed in terms of the log-likelihood-ratio cost (Cllr) and the 95% credible interval (CI), which are the metrics of validity and reliability, respectively. We will demonstrate in this paper that 1) the validity of the system notably improves when up to six tokens are included in modelling a speaker session, and 2) the system performance converges with the relative small token number (four) in the background database, regardless of the token numbers in the test and development databases.