  Speaker Diarization (SD) aims at grouping speech segments that belong to the
same speaker. This task is required in many speech-processing applications,
such as rich meeting transcription. In this context, distant microphone arrays
usually capture the audio signal. Beamforming, i.e., spatial filtering, is a
common practice to process multi-microphone audio data. However, it often
requires an explicit localization of the active source to steer the filter.
This paper proposes a self-attention-based algorithm to select the output of a
bank of fixed spatial filters. This method serves as a feature extractor for
joint Voice Activity (VAD) and Overlapped Speech Detection (OSD). The speaker
diarization is then inferred from the detected segments. The approach shows
convincing distant VAD, OSD, and SD performance, e.g. 14.5% DER on the
AISHELL-4 dataset. The analysis of the self-attention weights demonstrates
their explainability, as they correlate with the speaker's angular locations.
