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Our focus lies in developing an online speaker diarisation framework whichdemonstrates robust performance across diverse domains. In online speakerdiarisation, outputs generated in real-time are irreversible, and a fewmisjudgements in the early phase of an input session can lead to catastrophicresults. We hypothesise that cautiously increasing the number of estimatedspeakers is of paramount importance among many other factors. Thus, ourproposed framework includes decreasing the number of speakers by one when thesystem judges that an increase in the past was faulty. We also adopt dualbuffers, checkpoints and centroids, where checkpoints are combined withsilhouette coefficients to estimate the number of speakers and centroidsrepresent speakers. Again, we believe that more than one centroid can begenerated from one speaker. Thus we design a clustering-based label matchingtechnique to assign labels in real-time. The resulting system is lightweightyet surprisingly effective. The system demonstrates state-of-the-artperformance on DIHARD 2 and 3 datasets, where it is also competitive in AMI andVoxConverse test sets.

Absolute decision corrupts absolutely: conservative online speaker diarisation