  Several advances have been made recently towards handling overlapping speech
for speaker diarization. Since speech and natural language tasks often benefit
from ensemble techniques, we propose an algorithm for combining outputs from
such diarization systems through majority voting. Our method, DOVER-Lap, is
inspired from the recently proposed DOVER algorithm, but is designed to handle
overlapping segments in diarization outputs. We also modify the pair-wise
incremental label mapping strategy used in DOVER, and propose an approximation
algorithm based on weighted k-partite graph matching, which performs this
mapping using a global cost tensor. We demonstrate the strength of our method
by combining outputs from diverse systems -- clustering-based, region proposal
networks, and target-speaker voice activity detection -- on AMI and LibriCSS
datasets, where it consistently outperforms the single best system.
Additionally, we show that DOVER-Lap can be used for late fusion in
multichannel diarization, and compares favorably with early fusion methods like
beamforming.
