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We introduce DIVE, an end-to-end speaker diarization algorithm. Our neuralalgorithm presents the diarization task as an iterative process: it repeatedlybuilds a representation for each speaker before predicting the voice activityof each speaker conditioned on the extracted representations. This strategyintrinsically resolves the speaker ordering ambiguity without requiring theclassical permutation invariant training loss. In contrast with prior work, ourmodel does not rely on pretrained speaker representations and optimizes allparameters of the system with a multi-speaker voice activity loss. Importantly,our loss explicitly excludes unreliable speaker turn boundaries from training,which is adapted to the standard collar-based Diarization Error Rate (DER)evaluation. Overall, these contributions yield a system redefining thestate-of-the-art on the standard CALLHOME benchmark, with 6.7% DER compared to7.8% for the best alternative.

DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding