# A Unified Uncertainty-Aware Back-End for Speaker Verification: Scoring, Normalization, and Calibration

arXiv: https://arxiv.org/abs/2609.01221 · HTML: https://mlsanity.com/paper/2609.01221/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 13 of 13)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit](https://mlsanity.com/paper/2210.17016/) (2022)
- [The IDLAB VoxSRC-20 Submission: Large Margin Fine-Tuning and Quality-Aware Score Calibration in DNN Based Speaker Verification](https://mlsanity.com/paper/2010.11255/) (2020)
- [Xi-Vector Embedding for Speaker Recognition](https://mlsanity.com/paper/2108.05679/) (2021)
- [Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning](https://mlsanity.com/paper/2407.15188/) (2024)
- [Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?](https://mlsanity.com/paper/2204.03965/) (2022)
- [Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification](https://mlsanity.com/paper/2302.11763/) (2023)
- [Xi+: Uncertainty Supervision for Robust Speaker Embedding](https://mlsanity.com/paper/2509.05993/) (2025)
- [Cosine Scoring with Uncertainty for Neural Speaker Embedding](https://mlsanity.com/paper/2403.06404/) (2024)
- [U3-xi: Pushing the Boundaries of Speaker Recognition by Incorporating Uncertainty](https://mlsanity.com/paper/2601.15719/) (2026)
- [Towards Robust Uncertainty-Aware Speaker Modeling](https://mlsanity.com/paper/2607.04937/) (2026)

## Abstract (arXiv)

Speaker verification back-ends commonly combine similarity scoring, score normalization, and calibration. However, speaker embeddings extracted from real-world utterances have trial-dependent reliability because of factors such as duration, noise, and channel variation. Existing uncertainty-aware methods primarily improve the speaker encoder or the initial similarity score, while the estimated uncertainty is typically not propagated through subsequent normalization and calibration. We represent each utterance by a speaker embedding, interpreted as a posterior mean, together with its covariance as an uncertainty estimate. We present a unified uncertainty-aware back-end comprising uncertainty-aware cosine scoring, uncertainty-aware AS-Norm (UAS-Norm), and uncertainty-aware Quality Measure Function calibration (UQMF). Covariance information is incorporated throughout this pipeline to adjust score scaling, cohort statistics, normalized-score combination, and calibration features. Experiments with ECAPA-TDNN and ResNet show consistent EER reductions and improved target--non-target separation across both architectures.