  Automatic speaker diarization techniques typically involve a two-stage
processing approach where audio segments of fixed duration are converted to
vector representations in the first stage. This is followed by an unsupervised
clustering of the representations in the second stage. In most of the prior
approaches, these two stages are performed in an isolated manner with
independent optimization steps. In this paper, we propose a representation
learning and clustering algorithm that can be iteratively performed for
improved speaker diarization. The representation learning is based on
principles of self-supervised learning while the clustering algorithm is a
graph structural method based on path integral clustering (PIC). The
representation learning step uses the cluster targets from PIC and the
clustering step is performed on embeddings learned from the self-supervised
deep model. This iterative approach is referred to as self-supervised
clustering (SSC). The diarization experiments are performed on CALLHOME and AMI
meeting datasets. In these experiments, we show that the SSC algorithm improves
significantly over the baseline system (relative improvements of 13% and 59% on
CALLHOME and AMI datasets respectively in terms of diarization error rate
(DER)). In addition, the DER results reported in this work improve over several
other recent approaches for speaker diarization.
