  Over the last few years, deep learning has grown in popularity for speaker
verification, identification, and diarization. Inarguably, a significant part
of this success is due to the demonstrated effectiveness of their speaker
representations. These, however, are heavily dependent on large amounts of
annotated data and can be sensitive to new domains. This study proposes an
entirely unsupervised deep-learning model for speaker diarization.
Specifically, the study focuses on generating high-quality neural speaker
representations without any annotated data, as well as on estimating secondary
hyperparameters of the model without annotations.
  The speaker embeddings are represented by an encoder trained in a
self-supervised fashion using pairs of adjacent segments assumed to be of the
same speaker. The trained encoder model is then used to self-generate
pseudo-labels to subsequently train a similarity score between different
segments of the same call using probabilistic linear discriminant analysis
(PLDA) and further to learn a clustering stopping threshold. We compared our
model to state-of-the-art unsupervised as well as supervised baselines on the
CallHome benchmarks. According to empirical results, our approach outperforms
unsupervised methods when only two speakers are present in the call, and is
only slightly worse than recent supervised models.
