  Overlapping speech diarization has been traditionally treated as a
multi-label classification problem. In this paper, we reformulate this task as
a single-label prediction problem by encoding multiple binary labels into a
single label with the power set, which represents the possible combinations of
target speakers. This formulation has two benefits. First, the overlaps of
target speakers are explicitly modeled. Second, threshold selection is no
longer needed. Through this formulation, we propose the speaker embedding-aware
neural diarization (SEND) framework, where a speech encoder, a speaker encoder,
two similarity scorers, and a post-processing network are jointly optimized to
predict the encoded labels according to the similarities between speech
features and speaker embeddings. Experimental results show that SEND has a
stable learning process and can be trained on highly overlapped data without
extra initialization. More importantly, our method achieves the
state-of-the-art performance in real meeting scenarios with fewer model
parameters and lower computational complexity.
