  Combining end-to-end neural speaker diarization (EEND) with vector clustering
(VC), known as EEND-VC, has gained interest for leveraging the strengths of
both methods. EEND-VC estimates activities and speaker embeddings for all
speakers within an audio chunk and uses VC to associate these activities with
speaker identities across different chunks. EEND-VC generates thus multiple
streams of embeddings, one for each speaker in a chunk. We can cluster these
embeddings using constrained agglomerative hierarchical clustering (cAHC),
ensuring embeddings from the same chunk belong to different clusters. This
paper introduces an alternative clustering approach, a multi-stream extension
of the successful Bayesian HMM clustering of x-vectors (VBx), called MS-VBx.
Experiments on three datasets demonstrate that MS-VBx outperforms cAHC in
diarization and speaker counting performance.
