Simultaneous speech in meeting environment is responsible
for a certain amount of errors caused by standard speaker
diarization systems. We are presenting an overlap detection
system for far-field data based on spectral and spatial features,
where the spatial features obtained on different microphone
pairs are fused by means of principal component analysis. Detected
overlap segments are applied for speaker diarization in
order to increase the purity of speaker clusters and to recover
missed speech by assigning multiple speaker labels. Investigation
on the relationship between overlap detection properties
and diarization improvement revealed very distinct behaviour
of overlap exclusion and overlap labeling.Postprint (published version
