text: 183,151,1324354758463918096,76009287,Desh Raj,"🎉 New paper on ArXiv 🎉 DOVER-Lap: A method for combining overlap-aware diarization outputs Paper: <LINK> Code: <LINK> Thread about the work 👇 1/n Ensembles usually improve over single-best systems. But ensembling diarization (""who spoke when"") systems is hard for 2 reasons: 1. Output labels are not aligned across systems 2. Overlap-aware systems may predict multiple labels in a region 2/n We propose a method to achieve this combination in two stages: (i) mapping all system outputs to a common label space, and (ii) rank-weighted voting which considers overlapping speakers. 3/n We formulate (i) as a weighted k-partite graph matching problem, and propose a greedy maximal matching algorithm based on a ""global cost tensor"" containing all pairwise distances between predicted labels. 4/n <LINK> For (ii), we vote among the systems and predict the top N labels, where N is the weighted mean of number of labels rounded to the nearest integer. In simple words, if systems A, B, and C predict 2, 1, and 2 speakers in a region, the combined output contains 2 speakers. 5/n <LINK> We combined outputs from several new diarization methods (RPN, TS-VAD, etc.) -&gt; consistent improvements across different overlap conditions (see results on LibriCSS in the table): 6/n <LINK> More experiments in paper: - Combining systems on AMI meeting data - Using DOVER-Lap for multi-channel fusion (TL;DR -&gt; does better than WPE + beamforming) 7/n No training or GPUs needed! I did all the experiments on my MacBook Pro CPU and they ran in a few seconds. Consider using DOVER-Lap when you are submitting your diarization systems to future editions of DIHARD or VoxSRC 😀 n/n",https://arxiv.org/abs/2011.01997,"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. ",DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs,8,"['🎉 New paper on ArXiv 🎉\n\nDOVER-Lap: A method for combining overlap-aware diarization outputs\n\nPaper: <LINK>\nCode: <LINK>\n\nThread about the work 👇\n\n1/n', 'Ensembles usually improve over single-best systems. But ensembling diarization (""who spoke when"") systems is hard  for 2 reasons:\n\n1. Output labels are not aligned across systems\n2. Overlap-aware systems may predict multiple labels in a region\n\n2/n', 'We propose a method to achieve this combination in two stages: (i) mapping all system outputs to a common label space, and (ii) rank-weighted voting which considers overlapping speakers.\n\n3/n', 'We formulate (i) as a weighted k-partite graph matching problem, and propose a greedy maximal matching algorithm based on a ""global cost tensor"" containing all pairwise distances between predicted labels.\n\n4/n https://t.co/A48IjoIELM', 'For (ii), we vote among the systems and predict the top N labels, where N is the weighted mean of number of labels rounded to the nearest integer. In simple words, if systems A, B, and C predict 2, 1, and 2 speakers in a region, the combined output contains 2 speakers.\n\n5/n https://t.co/d2acDXCGMT', 'We combined outputs from several new diarization methods (RPN, TS-VAD, etc.) -&gt; consistent improvements across different overlap conditions (see results on LibriCSS in the table):\n\n6/n https://t.co/HuFc7174Fs', 'More experiments in paper: \n- Combining systems on AMI meeting data\n- Using DOVER-Lap for multi-channel fusion (TL;DR -&gt; does better than WPE + beamforming)\n\n7/n', 'No training or GPUs needed! I did all the experiments on my MacBook Pro CPU and they ran in a few seconds.\n\nConsider using DOVER-Lap when you are submitting your diarization systems to future editions of DIHARD or VoxSRC 😀\n\nn/n']",20,11,1662