Likelihood Ratio Calculation in Acoustic-Phonetic Forensic Voice Comparison: Comparison of Three Statistical Modelling Approaches

Abstract

This study compares three statistical models used to calculate likelihood ratios in acoustic-phonetic forensic-voicecomparison systems: Multivariate kernel density, principal component analysis kernel density, and a multivariate normal model. The data were coefficient values obtained from discrete cosine transforms fitted to human-supervised formant-trajectory measurements of tokens of /iau/ from a database of recordings of 60 female speakers of Chinese. Tests were conducted using high-quality recordings as nominal suspect samples and mobileto-landline transmitted recordings as nominal offender samples. Performance was assessed before and after fusion with a baseline automatic mel frequency cepstral coefficient Gaussian mixture model universal background model system. In addition, Monte Carlo simulations were used to compare the output of the statistical models to true likelihood-ratio values calculated on the basis of the distribution specified for a simulated population.

A Neural Network Approach to Writer’s Model for Full Likelihood Ratio in Handwriting Analysis

“…To determine whether a document is genuine, an examiner may attempt to confirm who created the document amongst other things. The likelihood ratio paradigm has been studied as a means for quantifying the strength of evidence for a variety of forensic evidence types in handwriting and other types of forensic evidence such as earmark, speech, footprint, fingerprint, glass fragments and DNA [1][2][3][4][5][6][7][8].…”