– Numerous studies in this field centre around developing a re-segmentation strategy
for diarization systems that can effectively handle both voice activity and overlapped
speech detection. This approach can also be a post-processing step to identify and
assign overlapped speech regions accurately. Notable examples of such works include
those by Bullock et al. [47] and Bredin and Laurent [45].
• End-to-End Neural Diarization: In addition to the above work, end-to-end speaker diarization
systems have gained the attention of the research community due to their ability to handle
speaker overlaps and their optimization to minimize diarization errors directly. In one such
work, the authors propose end-to-end neural speaker diarization that does not rely on
clustering and instead uses a self-attention-based neural network to directly output the
joint speech activities of all speakers for each segment [145]. Following the trend, several
AReviewofDeepLearningTechniquesforSpeechProcessing