Automatic forensic voice comparison (FVC) systems employed in forensic casework have often relied on Gaussian\nMixture Model - Universal Background Models (GMM-UBMs) for modelling with relatively little research into\nsupervector-based approaches. This paper reports on a comparative study which investigates the effectiveness of\nmultiple approaches operating on GMM mean supervectors, including support vector machines and various forms\nof regression. Firstly, we demonstrate a method by which supervector regression can be used to produce a forensic\nlikelihood ratio. Then, three variants of solving the regression problem are considered, namely least squares and ?1\nand ?2 norm minimization solutions. Comparative analysis of these techniques, combined with four different scoring\nmethods, reveals that supervector regression can provide a substantial relative improvement in both validity (up to\n75.3%) and reliability (up to 41.5%) over both Gaussian Mixture Model - Universal Background Models (GMM-UBMs)\nand Gaussian Mixture Model - Support Vector Machine (GMM-SVM) results when evaluated on two studio clean\nforensic speech databases. Under mismatched/noisy conditions, more modest relative improvements in both validity\n(up to 41.5%) and reliability (up to 12.1%) were obtained relative to GMM-SVM results. From a practical standpoint,\nthe analysis also demonstrates that supervector regression can be more effective than GMM-UBM or GMM-SVM in\nobtaining a higher positive-valued likelihood ratio for same-speaker comparisons, thus improving the strength of\nevidence if the particular suspect on trial is indeed the offender. Based on these results, we recommend least\nsquares as the better performing regression technique with gradient projection as another promising technique\nspecifically for applications typical of forensic case work.
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