# The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA

## TL;DR

This paper introduces CORAL+, an unsupervised domain adaptation algorithm for PLDA in speaker recognition, effectively reducing domain mismatch using unlabeled in-domain data, validated on NIST SRE datasets.

## Contribution

It presents a novel model-based adaptation method called CORAL+ that extends feature-based CORAL for unsupervised PLDA adaptation in speaker recognition.

## Key findings

- CORAL+ improves speaker recognition accuracy on NIST SRE datasets.
- The method effectively adapts to domain mismatch with unlabeled data.
- Experimental results show significant performance gains.

## Abstract

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.