  In this paper, we apply the variational information bottleneck approach to
end-to-end neural diarization with encoder-decoder attractors (EEND-EDA). This
allows us to investigate what information is essential for the model. EEND-EDA
utilizes attractors, vector representations of speakers in a conversation. Our
analysis shows that, attractors do not necessarily have to contain speaker
characteristic information. On the other hand, giving the attractors more
freedom to allow them to encode some extra (possibly speaker-specific)
information leads to small but consistent diarization performance improvements.
Despite architectural differences in EEND systems, the notion of attractors and
frame embeddings is common to most of them and not specific to EEND-EDA. We
believe that the main conclusions of this work can apply to other variants of
EEND. Thus, we hope this paper will be a valuable contribution to guide the
community to make more informed decisions when designing new systems.
