# Probabilistic Back-ends for Online Speaker Recognition and Clustering

arXiv: https://arxiv.org/abs/2302.09523 · HTML: https://mlsanity.com/paper/2302.09523/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 19 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Spot the conversation: speaker diarisation in the wild](https://mlsanity.com/paper/2007.01216/) (2020)
- [Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks](https://mlsanity.com/paper/2012.14952/) (2020)
- [The Second DIHARD Diarization Challenge: Dataset, task, and baselines](https://mlsanity.com/paper/1906.07839/) (2019)
- [Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2009.14153/) (2020)
- [Attention Back-end for Automatic Speaker Verification with Multiple Enrollment Utterances](https://mlsanity.com/paper/2104.01541/) (2021)
- [Links: A High-Dimensional Online Clustering Method](https://mlsanity.com/paper/1801.10123/) (2018)
- [Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?](https://mlsanity.com/paper/2204.03965/) (2022)
- [Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets](https://mlsanity.com/paper/2109.02576/) (2021)
- [Magnitude-aware Probabilistic Speaker Embeddings](https://mlsanity.com/paper/2202.13826/) (2022)
- [Baselines and Protocols for Household Speaker Recognition](https://mlsanity.com/paper/2205.00288/) (2022)
- [Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings](https://mlsanity.com/paper/2203.14893/) (2022)
- [Unifying Cosine and PLDA Back-ends for Speaker Verification](https://mlsanity.com/paper/2204.10523/) (2022)

## Built on by (most cited first, 1 of 1)

- [DG^VoiC: Speaker Clustering for Fraud Investigation under Real Call-Centre Conditions](https://mlsanity.com/paper/2606.28048/) (2026)

## Abstract (arXiv)

This paper focuses on multi-enrollment speaker recognition which naturally occurs in the task of online speaker clustering, and studies the properties of different scoring back-ends in this scenario. First, we show that popular cosine scoring suffers from poor score calibration with a varying number of enrollment utterances. Second, we propose a simple replacement for cosine scoring based on an extremely constrained version of probabilistic linear discriminant analysis (PLDA). The proposed model improves over the cosine scoring for multi-enrollment recognition while keeping the same performance in the case of one-to-one comparisons. Finally, we consider an online speaker clustering task where each step naturally involves multi-enrollment recognition. We propose an online clustering algorithm allowing us to take benefits from the PLDA model such as the ability to handle uncertainty and better score calibration. Our experiments demonstrate the effectiveness of the proposed algorithm.