  In this paper, we propose a novel algorithm for speaker diarization using
metric learning for graph based clustering. The graph clustering algorithms use
an adjacency matrix consisting of similarity scores. These scores are computed
between speaker embeddings extracted from pairs of audio segments within the
given recording. In this paper, we propose an approach that jointly learns the
speaker embeddings and the similarity metric using principles of
self-supervised learning. The metric learning network implements a neural model
of the probabilistic linear discriminant analysis (PLDA). The self-supervision
is derived from the pseudo labels obtained from a previous iteration of
clustering. The entire model of representation learning and metric learning is
trained with a binary cross entropy loss. By combining the self-supervision
based metric learning along with the graph-based clustering algorithm, we
achieve significant relative improvements of 60% and 7% over the x-vector PLDA
agglomerative hierarchical clustering (AHC) approach on AMI and the DIHARD
datasets respectively in terms of diarization error rates (DER).
