  Speaker diarization is a task to label audio or video recordings with classes
that correspond to speaker identity, or in short, a task to identify "who spoke
when". In the early years, speaker diarization algorithms were developed for
speech recognition on multispeaker audio recordings to enable speaker adaptive
processing. These algorithms also gained their own value as a standalone
application over time to provide speaker-specific metainformation for
downstream tasks such as audio retrieval. More recently, with the emergence of
deep learning technology, which has driven revolutionary changes in research
and practices across speech application domains, rapid advancements have been
made for speaker diarization. In this paper, we review not only the historical
development of speaker diarization technology but also the recent advancements
in neural speaker diarization approaches. Furthermore, we discuss how speaker
diarization systems have been integrated with speech recognition applications
and how the recent surge of deep learning is leading the way of jointly
modeling these two components to be complementary to each other. By considering
such exciting technical trends, we believe that this paper is a valuable
contribution to the community to provide a survey work by consolidating the
recent developments with neural methods and thus facilitating further progress
toward a more efficient speaker diarization.
