  For online speaker diarization, samples arrive incrementally, and the overall
distribution of the samples is invisible. Moreover, in most existing
clustering-based methods, the training objective of the embedding extractor is
not designed specially for clustering. To improve online speaker diarization
performance, we propose a unified online clustering framework, which provides
an interactive manner between embedding extractors and clustering algorithms.
Specifically, the framework consists of two highly coupled parts:
clustering-guided recurrent training (CGRT) and truncated beam searching
clustering (TBSC). The CGRT introduces the clustering algorithm into the
training process of embedding extractors, which could provide not only
cluster-aware information for the embedding extractor, but also crucial
parameters for the clustering process afterward. And with these parameters,
which contain preliminary information of the metric space, the TBSC penalizes
the probability score of each cluster, in order to output more accurate
clustering results in online fashion with low latency. With the above
innovations, our proposed online clustering system achieves 14.48\% DER with
collar 0.25 at 2.5s latency on the AISHELL-4, while the DER of the offline
agglomerative hierarchical clustering is 14.57\%.
