This thesis concerns the detection of overlapping speech segments and its further application for the improvement of speaker diarization performance. We propose the use of three spatial cross-correlation-based parameters for overlap detection on distant microphone channel data. Spatial features from dierent microphone pairs are fused by means of principal component analysis or by an approach involving a multilayer perceptron. In addition, we investigate the possibility of employing long-term prosodic information. The most suitable subset of candidate prosodic features is determined by a two-step mRMR feature selection algorithm. For segments including detected overlapping speech the speaker diarization system picks a second speaker label, and such segments are
also discarded from the model training. The proposed overlap labeling technique is integrated in the Viterbi-decoding part of the diarization algorithm.Peer ReviewedPostprint (published version
