A comparison of procedures for the calculation of forensic likelihood ratios from acoustic–phonetic data: Multivariate kernel density (MVKD) versus Gaussian mixture model–universal background model (GMM–UBM)

Abstract

Two procedures for the calculation of forensic likelihood ratios were tested on the same set of acoustic–phonetic data. One procedure was a multivariate kernel density procedure (MVKD) which is common in acoustic–phonetic forensic voice comparison, and the other was a Gaussian mixture model–universal background model (GMM–UBM) which is common in automatic forensic voice comparison. The data were coefficient values from discrete cosine transforms fitted to second-formant trajectories of /aɪ/, /eɪ/, /oʊ/, /aʊ/, and /ɔɪ/ tokens produced by 27 male speakers of Australian English. Scores were calculated separately for each phoneme and then fused using logistic regression. The performance of the fused GMM–UBM system was much better than that of the fused MVKD system, both in terms of accuracy (as measured using the log-likelihood-ratio cost, C llr ) and precision (as measured using an empirical estimate of the 95% credible interval for the likelihood ratios from the different-speaker comparisons).