A substantial portion of errors of the conventional speaker
diarization systems on meeting data can be accounted to overlapped
speech. This paper proposes the use of several spatial
features to improve speech overlap detection on distant channel
microphones. These spatial features are integrated into a
spectral-based system by using principal component analysis
and neural networks. Different overlap detection hypotheses
are used to improve diarization performance with both overlap
exclusion and overlap labeling. In experiments conducted
on AMI Meeting Corpus we demonstrate a relative DER improvement
of 11.6% and 14.6% for single- and multi-site data,
respectively