  We propose a modified teacher-student training for the extraction of
frame-wise speaker embeddings that allows for an effective diarization of
meeting scenarios containing partially overlapping speech. To this end, a
geodesic distance loss is used that enforces the embeddings computed from
regions with two active speakers to lie on the shortest path on a sphere
between the points given by the d-vectors of each of the active speakers. Using
those frame-wise speaker embeddings in clustering-based diarization outperforms
segment-level clustering-based diarization systems such as VBx and Spectral
Clustering. By extending our approach to a mixture-model-based diarization, the
performance can be further improved, approaching the diarization error rates of
diarization systems that use a dedicated overlap detection, and outperforming
these systems when also employing an additional overlap detection.
