Absolute decision corrupts absolutely: conservative online speaker diarisation
Our focus lies in developing an online speaker diarisation framework which demonstrates robust performance across diverse domains. In online speaker diarisation, outputs generated in real-time are irreversible, and a few misjudgements in the early phase of an input session can lead to catastrophic r...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
09.11.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Our focus lies in developing an online speaker diarisation framework which
demonstrates robust performance across diverse domains. In online speaker
diarisation, outputs generated in real-time are irreversible, and a few
misjudgements in the early phase of an input session can lead to catastrophic
results. We hypothesise that cautiously increasing the number of estimated
speakers is of paramount importance among many other factors. Thus, our
proposed framework includes decreasing the number of speakers by one when the
system judges that an increase in the past was faulty. We also adopt dual
buffers, checkpoints and centroids, where checkpoints are combined with
silhouette coefficients to estimate the number of speakers and centroids
represent speakers. Again, we believe that more than one centroid can be
generated from one speaker. Thus we design a clustering-based label matching
technique to assign labels in real-time. The resulting system is lightweight
yet surprisingly effective. The system demonstrates state-of-the-art
performance on DIHARD 2 and 3 datasets, where it is also competitive in AMI and
VoxConverse test sets. |
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DOI: | 10.48550/arxiv.2211.04768 |