# The distribution of calibrated likelihood-ratios in speaker recognition

arXiv: https://arxiv.org/abs/1304.1199 · HTML: https://mlsanity.com/paper/1304.1199/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2013.

## Built on by (most cited first, 5 of 5)

- [A Speaker Verification Backend with Robust Performance across Conditions](https://mlsanity.com/paper/2102.01760/) (2021)
- [Shortcut Learning in Binary Classifier Black Boxes: Applications to Voice Anti-Spoofing and Biometrics](https://mlsanity.com/paper/2601.17782/) (2026)
- [Out of a hundred trials, how many errors does your speaker verifier make?](https://mlsanity.com/paper/2104.00732/) (2021)
- [t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators](https://mlsanity.com/paper/2309.12237/) (2023)
- [The distribution of calibrated likelihood functions on the probability-likelihood Aitchison simplex](https://mlsanity.com/paper/2509.03365/) (2025)

## Abstract (arXiv)

This paper studies properties of the score distributions of calibrated log-likelihood-ratios that are used in automatic speaker recognition. We derive the essential condition for calibration that the log likelihood ratio of the log-likelihood-ratio is the log-likelihood-ratio. We then investigate what the consequence of this condition is to the probability density functions (PDFs) of the log-likelihood-ratio score. We show that if the PDF of the non-target distribution is Gaussian, then the PDF of the target distribution must be Gaussian as well. The means and variances of these two PDFs are interrelated, and determined completely by the discrimination performance of the recognizer characterized by the equal error rate. These relations allow for a new way of computing the offset and scaling parameters for linear calibration, and we derive closed-form expressions for these and show that for modern i-vector systems with PLDA scoring this leads to good calibration, comparable to traditional logistic regression, over a wide range of system performance.