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Several advances have been made recently towards handling overlapping speechfor speaker diarization. Since speech and natural language tasks often benefitfrom ensemble techniques, we propose an algorithm for combining outputs fromsuch diarization systems through majority voting. Our method, DOVER-Lap, isinspired from the recently proposed DOVER algorithm, but is designed to handleoverlapping segments in diarization outputs. We also modify the pair-wiseincremental label mapping strategy used in DOVER, and propose an approximationalgorithm based on weighted k-partite graph matching, which performs thismapping using a global cost tensor. We demonstrate the strength of our methodby combining outputs from diverse systems -- clustering-based, region proposalnetworks, and target-speaker voice activity detection -- on AMI and LibriCSSdatasets, where it consistently outperforms the single best system.Additionally, we show that DOVER-Lap can be used for late fusion inmultichannel diarization, and compares favorably with early fusion methods likebeamforming.

DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs