  In this paper, we propose an iterative framework for self-supervised speaker
representation learning based on a deep neural network (DNN). The framework
starts with training a self-supervision speaker embedding network by maximizing
agreement between different segments within an utterance via a contrastive
loss. Taking advantage of DNN's ability to learn from data with label noise, we
propose to cluster the speaker embedding obtained from the previous speaker
network and use the subsequent class assignments as pseudo labels to train a
new DNN. Moreover, we iteratively train the speaker network with pseudo labels
generated from the previous step to bootstrap the discriminative power of a
DNN. Speaker verification experiments are conducted on the VoxCeleb dataset.
The results show that our proposed iterative self-supervised learning framework
outperformed previous works using self-supervision. The speaker network after 5
iterations obtains a 61% performance gain over the speaker embedding model
trained with contrastive loss.
