  Recently, hybrid systems of clustering and neural diarization models have
been successfully applied in multi-party meeting analysis. However, current
models always treat overlapped speaker diarization as a multi-label
classification problem, where speaker dependency and overlaps are not well
considered. To overcome the disadvantages, we reformulate overlapped speaker
diarization task as a single-label prediction problem via the proposed power
set encoding (PSE). Through this formulation, speaker dependency and overlaps
can be explicitly modeled. To fully leverage this formulation, we further
propose the speaker overlap-aware neural diarization (SOND) model, which
consists of a context-independent (CI) scorer to model global speaker
discriminability, a context-dependent scorer (CD) to model local
discriminability, and a speaker combining network (SCN) to combine and reassign
speaker activities. Experimental results show that using the proposed
formulation can outperform the state-of-the-art methods based on target speaker
voice activity detection, and the performance can be further improved with
SOND, resulting in a 6.30% relative diarization error reduction.
