A Comparative Study of Two Statistical Modelling Approaches for Estimating Multivariate Likelihood Ratios in Forensic Voice Comparison

摘要

The acoustic features used in forensic voice comparison (FVC) are correlated in almost all cases. A sizeable proportion of FVC studies and casework has relied, for statistical modelling, on the multivariate kernel density likelihood ratio (MVKDLR) formula, which considers the correlations between the features and computes an overall combined likelihood ratio (LR) for the offender-suspect comparison. However, following concerns over the robustness of the MVKDLR, in particular its computational weakness and numerical instability specifically when a large number of features are employed, the principal component analysis kernel density likelihood ratio (PCAKDLR) approach was developed as an alternative. In this study, the performance of the two approaches is investigated and compared using Monte Carlo-simulated synthetic data based on the 16~(th)-order Mel Frequency Cepstrum Coefficients extracted from the long vowel /e:/ segments of spontaneous speech uttered by 118 native Japanese male speakers. Performance is assessed in terms of validity (= accuracy) and reliability (= precision), with the log-likelihood ratio cost (C_(llr)) being used to assess validity and the 95% credible interval (95%CI) to assess reliability.