Iterative self-training, or iterative pseudo-labeling (IPL) — using an improved model from the current iteration to provide pseudo-labels for the next iteration — has proven to be a powerful approach to enhance the quality of speaker representations. Recent applications of IPL in unsupervised speaker recognition start with representations extracted from very elaborate self-supervised methods (e.g., DINO). However, training such strong self-supervised models is not straightforward (they require hyper-parameter tuning and may not generalize to out-of-domain data) and, moreover, may not be…Apple Machine Learning Research