  In this paper, different online speaker diarization systems are evaluated on
the same hardware with the same test data with regard to their latency. The
latency is the time span from audio input to the output of the corresponding
speaker label. As part of the evaluation, various model combinations within the
DIART framework, a diarization system based on the online clustering algorithm
UIS-RNN-SML, and the end-to-end online diarization system FS-EEND are compared.
The lowest latency is achieved for the DIART-pipeline with the embedding model
pyannote/embedding and the segmentation model pyannote/segmentation. The
FS-EEND system shows a similarly good latency. In general there is currently no
published research that compares several online diarization systems in terms of
their latency. This makes this work even more relevant.
