  Speaker diarization is the task of answering Who spoke and when? in an audio
stream. Pipeline systems rely on speech segmentation to extract speakers'
segments and achieve robust speaker diarization. This paper proposes a common
framework to solve three segmentation tasks in the distant speech scenario:
Voice Activity Detection (VAD), Overlapped Speech Detection (OSD), and Speaker
Change Detection (SCD). In the literature, a few studies investigate the
multi-microphone distant speech scenario. In this work, we propose a new set of
spatial features based on direction-of-arrival estimations in the circular
harmonic domain (CH-DOA). These spatial features are extracted from
multi-microphone audio data and combined with standard acoustic features.
Experiments on the AMI meeting corpus show that CH-DOA can improve the
segmentation while being robust in the case of deactivated microphones.
