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Recently, hybrid systems of clustering and neural diarization models havebeen successfully applied in multi-party meeting analysis. However, currentmodels always treat overlapped speaker diarization as a multi-labelclassification problem, where speaker dependency and overlaps are not wellconsidered. To overcome the disadvantages, we reformulate overlapped speakerdiarization task as a single-label prediction problem via the proposed powerset encoding (PSE). Through this formulation, speaker dependency and overlapscan be explicitly modeled. To fully leverage this formulation, we furtherpropose the speaker overlap-aware neural diarization (SOND) model, whichconsists of a context-independent (CI) scorer to model global speakerdiscriminability, a context-dependent scorer (CD) to model localdiscriminability, and a speaker combining network (SCN) to combine and reassignspeaker activities. Experimental results show that using the proposedformulation can outperform the state-of-the-art methods based on target speakervoice activity detection, and the performance can be further improved withSOND, resulting in a 6.30% relative diarization error reduction.

Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis