  In this paper, we propose a simple but powerful unsupervised learning method
for speaker recognition, namely Contrastive Equilibrium Learning (CEL), which
increases the uncertainty on nuisance factors latent in the embeddings by
employing the uniformity loss. Also, to preserve speaker discriminability, a
contrastive similarity loss function is used together. Experimental results
showed that the proposed CEL significantly outperforms the state-of-the-art
unsupervised speaker verification systems and the best performing model
achieved 8.01% and 4.01% EER on VoxCeleb1 and VOiCES evaluation sets,
respectively. On top of that, the performance of the supervised speaker
embedding networks trained with initial parameters pre-trained via CEL showed
better performance than those trained with randomly initialized parameters.
