# The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA

arXiv: https://arxiv.org/abs/1812.10260 · HTML: https://mlsanity.com/paper/1812.10260/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2018.

## Built on by (most cited first, 10 of 10)

- [VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2302.10248/) (2023)
- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [CATS: Mitigating Correlation Shift for Multivariate Time Series Classification](https://mlsanity.com/paper/2504.04283/) (2025)
- [Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation](https://mlsanity.com/paper/2203.10992/) (2022)
- [Robust Channel Learning for Large-Scale Radio Speaker Verification](https://mlsanity.com/paper/2406.10956/) (2024)
- [Partial Coupling of Optimal Transport for Spoken Language Identification](https://mlsanity.com/paper/2203.17036/) (2022)
- [Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification](https://mlsanity.com/paper/2305.12703/) (2023)
- [Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV Challenge Task 2](https://mlsanity.com/paper/2403.19634/) (2024)
- [Source -Free Domain Adaptation for Speaker Verification in Data-Scarce Languages and Noisy Channels](https://mlsanity.com/paper/2406.05863/) (2024)
- [Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification](https://mlsanity.com/paper/2606.22369/) (2026)

## Abstract (arXiv)

State-of-the-art speaker recognition systems comprise an x-vector (or i-vector) speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) backend. The effectiveness of these components relies on the availability of a large collection of labeled training data. In practice, it is common that the domains (e.g., language, demographic) in which the system are deployed differs from that we trained the system. To close the gap due to the domain mismatch, we propose an unsupervised PLDA adaptation algorithm to learn from a small amount of unlabeled in-domain data. The proposed method was inspired by a prior work on feature-based domain adaptation technique known as the correlation alignment (CORAL). We refer to the model-based adaptation technique proposed in this paper as CORAL+. The efficacy of the proposed technique is experimentally validated on the recent NIST 2016 and 2018 Speaker Recognition Evaluation (SRE'16, SRE'18) datasets.