# Generative Modelling for Unsupervised Score Calibration

arXiv: https://arxiv.org/abs/1311.0707 · HTML: https://mlsanity.com/paper/1311.0707/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2013.

## Built on by (most cited first, 3 of 3)

- [Analysis and Comparison of Classification Metrics](https://mlsanity.com/paper/2209.05355/) (2022)
- [Out of a hundred trials, how many errors does your speaker verifier make?](https://mlsanity.com/paper/2104.00732/) (2021)
- [Self-Tuning Spectral Clustering for Speaker Diarization](https://mlsanity.com/paper/2410.00023/) (2024)

## Abstract (arXiv)

Score calibration enables automatic speaker recognizers to make cost-effective accept / reject decisions. Traditional calibration requires supervised data, which is an expensive resource. We propose a 2-component GMM for unsupervised calibration and demonstrate good performance relative to a supervised baseline on NIST SRE'10 and SRE'12. A Bayesian analysis demonstrates that the uncertainty associated with the unsupervised calibration parameter estimates is surprisingly small.