  In real-world applications, speaker recognition models often face various
domain-mismatch challenges, leading to a significant drop in performance.
Although numerous domain adaptation techniques have been developed to address
this issue, almost all present methods focus on a simple configuration where
the model is trained in one domain and deployed in another. However, real-world
environments are often complex and may contain multiple domains, making the
methods designed for one-to-one adaptation suboptimal. In our paper, we propose
a self-supervised learning method to tackle this multi-domain adaptation
problem. Building upon the basic self-supervised adaptation algorithm, we
designed three strategies to make it suitable for multi-domain adaptation: an
in-domain negative sampling strategy, a MoCo-like memory bank scheme, and a
CORAL-like distribution alignment. We conducted experiments using VoxCeleb2 as
the source domain dataset and CN-Celeb1 as the target multi-domain dataset. Our
results demonstrate that our method clearly outperforms the basic
self-supervised adaptation method, which simply treats the data of CN-Celeb1 as
a single domain. Importantly, the improvement is consistent in nearly all
in-domain tests and cross-domain tests, demonstrating the effectiveness of our
proposed method.
