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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Bibliographic Details
Main Authors: Kwon, Youngki, Heo, Hee-Soo, Lee, Bong-Jin, Kim, You Jin, Jung, Jee-Weon
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
centroid
dual buffer
online speaker diarisation
Real-time systems
Signal processing
silhouette coefficient
Speech processing
Online Access:Request 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 realtime. The resulting system is lightweight yet surprisingly effective. The system demonstrates state-of-the-art performance on DIHARD II and III datasets, where it is also competitive in AMI and VoxConverse test sets.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10096276