# Bayesian calibration for forensic evidence reporting

arXiv: https://arxiv.org/abs/1403.5997 · HTML: https://mlsanity.com/paper/1403.5997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2014.

## Built on by (most cited first, 1 of 1)

- [Out of a hundred trials, how many errors does your speaker verifier make?](https://mlsanity.com/paper/2104.00732/) (2021)

## Abstract (arXiv)

We introduce a Bayesian solution for the problem in forensic speaker recognition, where there may be very little background material for estimating score calibration parameters. We work within the Bayesian paradigm of evidence reporting and develop a principled probabilistic treatment of the problem, which results in a Bayesian likelihood-ratio as the vehicle for reporting weight of evidence. We show in contrast, that reporting a likelihood-ratio distribution does not solve this problem. Our solution is experimentally exercised on a simulated forensic scenario, using NIST SRE'12 scores, which demonstrates a clear advantage for the proposed method compared to the traditional plugin calibration recipe.