Sequence-to-Sequence Refinement for EEND-VC Diarization with a Shared Encoder

Abstract

End-to-end neural diarization with vector clustering (EEND-VC) separates local segmentation from global speaker assignment, leaving speaker confusion (SC) despite strong segmentation. We propose shared-encoder sequence-to-sequence (S2S) refinement, which reuses DiariZen's self-supervised learning (SSL) representations for speaker-conditioned decoding and prior-constrained assignment. We study frozen-feature refinement with WavLM and structurally pruned w2v-BERT 2.0, and extend the latter to joint training of local segmentation and refinement heads. The heads learn separate layer mixtures while their losses update one acoustic encoder. Across eight domains, frozen-feature refinement reduces macro diarization error rate (DER) from 12.95% to 12.40% with WavLM and from 12.35% to 12.08% with w2v-BERT 2.0, with relative SC reductions of 17.4% and 14.6%. It adds approximately 15% to system real-time factor. The joint system further lowers macro DER to 11.90%, improving seven of eight domains over non-joint refinement. Error decomposition separates SC-driven refinement gains from detection-driven joint-system gains.