Diarization Clustering
The grouping of speech segments by speaker identity or embedding similarity during a speaker-diarization pipeline.
Speech Processing Context
Diarization clustering groups speaker embeddings or segment representations so that segments attributed to the same speaker receive the same anonymous label. Agglomerative, spectral, and probabilistic clustering methods are common, with threshold or model-selection choices becoming critical when speaker count is unknown.
Modeling Boundary
Strong speaker embeddings do not guarantee correct diarization. Segmentation errors, overlapped speech, short turns, and domain mismatch propagate into the clustering stage and can dominate the final error rate.