llreval 0.0.3

Python package for the log-likelihood-ratio

LLR-Evaluation (llreval)

This is an authorized fork from PYLLR.

Python toolkit for likelihood-ratio calibration of binary classifiers.

The emphasis is on binary classifiers (for example speaker verification), where the output of the classifier is in the form of a well-calibrated log-likelihood-ratio (LLR). The tools include:

• PAV and ROCCH score analysis.
• DET curves and EER
• DCF and minDCF
• Bayes error-rate plots
• Cllr

Most of the algorithms in LLR-Evaluation are Python translations of the older MATLAB BOSARIS Tookit. Descriptions of the algorithms are available in:

Niko Brümmer and Edward de Villiers, The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF, 2013.

Install

Install using pip

pip install llreval

Usage

import llreval

Out of a hundred trials, how many errors does your speaker verifier make?

We have included in the examples directory, some code that reproduces the plots in our paper:

Niko Brümmer, Luciana Ferrer and Albert Swart, "Out of a hundred trials, how many errors does your speaker verifier make?", 2011, https://arxiv.org/abs/2104.00732.

For instructions, go to the readme