  Using a Teacher-Student training approach we developed a speaker embedding
extraction system that outputs embeddings at frame rate. Given this high
temporal resolution and the fact that the student produces sensible speaker
embeddings even for segments with speech overlap, the frame-wise embeddings
serve as an appropriate representation of the input speech signal for an
end-to-end neural meeting diarization (EEND) system. We show in experiments
that this representation helps mitigate a well-known problem of EEND systems:
when increasing the number of speakers the diarization performance drop is
significantly reduced. We also introduce block-wise processing to be able to
diarize arbitrarily long meetings.
