# Investigation of Different Calibration Methods for Deep Speaker Embedding based Verification Systems

arXiv: https://arxiv.org/abs/2203.15106 · HTML: https://mlsanity.com/paper/2203.15106/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [BUT System Description to VoxCeleb Speaker Recognition Challenge 2019](https://mlsanity.com/paper/1910.12592/) (2019)
- [VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2012.06867/) (2020)
- [Deep Speaker Embeddings for Far-Field Speaker Recognition on Short Utterances](https://mlsanity.com/paper/2002.06033/) (2020)
- [On deep speaker embeddings for text-independent speaker recognition](https://mlsanity.com/paper/1804.10080/) (2018)
- [STC speaker recognition systems for the NIST SRE 2021](https://mlsanity.com/paper/2111.02298/) (2021)
- [Likelihood-ratio calibration using prior-weighted proper scoring rules](https://mlsanity.com/paper/1307.7981/) (2013)
- [A comparison of linear and non-linear calibrations for speaker recognition](https://mlsanity.com/paper/1402.2447/) (2014)
- [A discriminative condition-aware backend for speaker verification](https://mlsanity.com/paper/1911.11622/) (2019)

## Abstract (arXiv)

Deep speaker embedding extractors have already become new state-of-the-art systems in the speaker verification field. However, the problem of verification score calibration for such systems often remains out of focus. An irrelevant score calibration leads to serious issues, especially in the case of unknown acoustic conditions, even if we use a strong speaker verification system in terms of threshold-free metrics. This paper presents an investigation over several methods of score calibration: a classical approach based on the logistic regression model; the recently presented magnitude estimation network MagnetO that uses activations from the pooling layer of the trained deep speaker extractor and generalization of such approach based on separate scale and offset prediction neural networks. An additional focus of this research is to estimate the impact of score normalization on the calibration performance of the system. The obtained results demonstrate that there are no serious problems if in-domain development data are used for calibration tuning. Otherwise, a trade-off between good calibration performance and threshold-free system quality arises. In most cases using adaptive s-norm helps to stabilize score distributions and to improve system performance. Meanwhile, some experiments demonstrate that novel approaches have their limits in score stabilization on several datasets.