Tutorial on logistic-regression calibration and fusion: converting a score to a likelihood ratio

Geoffrey Stewart Morrison*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Logistic-regression calibration and fusion are potential steps in the calculation of forensic likelihood ratios. The present paper provides a tutorial on logistic-regression calibration and fusion at a practical conceptual level with minimal mathematical complexity. A score is log-likelihood-ratio like in that it indicates the degree of similarity of a pair of samples while taking into consideration their typicality with respect to a model of the relevant population. A higher-valued score provides more support for the same-origin hypothesis over the different-origin hypothesis than does a lower-valued score; however, the absolute values of scores are not interpretable as log likelihood ratios. Logistic-regression calibration is a procedure for converting scores to log likelihood ratios, and logistic-regression fusion is a procedure for converting parallel sets of scores from multiple forensic-comparison systems to log likelihood ratios. Logistic-regression calibration and fusion were developed for automatic speaker recognition and are popular in forensic voice comparison. They can also be applied in other branches of forensic science, a fingerprint/finger-mark example is provided.

Original languageEnglish
Pages (from-to)173-197
Number of pages25
JournalAustralian Journal of Forensic Sciences
Volume45
Issue number2
DOIs
Publication statusPublished - 1 Jun 2013

Keywords

  • Calibration
  • Forensic science
  • Fusion
  • Likelihood ratio
  • Logistic regression
  • Score

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