On the improvement of speaker diarization by detecting overlapped speech

Abstract

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.