  We propose to address online speaker diarization as a combination of
incremental clustering and local diarization applied to a rolling buffer
updated every 500ms. Every single step of the proposed pipeline is designed to
take full advantage of the strong ability of a recently proposed end-to-end
overlap-aware segmentation to detect and separate overlapping speakers. In
particular, we propose a modified version of the statistics pooling layer
(initially introduced in the x-vector architecture) to give less weight to
frames where the segmentation model predicts simultaneous speakers.
Furthermore, we derive cannot-link constraints from the initial segmentation
step to prevent two local speakers from being wrongfully merged during the
incremental clustering step. Finally, we show how the latency of the proposed
approach can be adjusted between 500ms and 5s to match the requirements of a
particular use case, and we provide a systematic analysis of the influence of
latency on the overall performance (on AMI, DIHARD and VoxConverse).
