  Current speaker diarization systems rely on an external voice activity
detection model prior to speaker embedding extraction on the detected speech
segments. In this paper, we establish that the attention system of a speaker
embedding extractor acts as a weakly supervised internal VAD model and performs
equally or better than comparable supervised VAD systems. Subsequently, speaker
diarization can be performed efficiently by extracting the VAD logits and
corresponding speaker embedding simultaneously, alleviating the need and
computational overhead of an external VAD model. We provide an extensive
analysis of the behavior of the frame-level attention system in current speaker
verification models and propose a novel speaker diarization pipeline using
ECAPA2 speaker embeddings for both VAD and embedding extraction. The proposed
strategy gains state-of-the-art performance on the AMI, VoxConverse and DIHARD
III diarization benchmarks.
