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We propose a modified teacher-student training for the extraction offrame-wise speaker embeddings that allows for an effective diarization ofmeeting scenarios containing partially overlapping speech. To this end, ageodesic distance loss is used that enforces the embeddings computed fromregions with two active speakers to lie on the shortest path on a spherebetween the points given by the d-vectors of each of the active speakers. Usingthose frame-wise speaker embeddings in clustering-based diarization outperformssegment-level clustering-based diarization systems such as VBx and SpectralClustering. By extending our approach to a mixture-model-based diarization, theperformance can be further improved, approaching the diarization error rates ofdiarization systems that use a dedicated overlap detection, and outperformingthese systems when also employing an additional overlap detection.

Geodesic interpolation of frame-wise speaker embeddings for the diarization of meeting scenarios