  Speaker diarization is a task to label audio or video recordings with classes
corresponding 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 multi-speaker audio recordings to enable speaker adaptive
processing, but also gained its own value as a stand-alone application over
time to provide speaker-specific meta information for downstream tasks such as
audio retrieval. More recently, with the rise of deep learning technology that
has been a driving force to revolutionary changes in research and practices
across speech application domains in the past decade, more 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. We also 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 it 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
towards a more efficient speaker diarization.
