Improved Overlapped Speech Handling For Speaker Diarization

We present our ongoing work in addressing the issue of overlapped speech in speaker diarization through the use of overlap segmentation, overlapped speech exclusion, and overlap segment labeling. Using feature analysis, we identify the most salient features from a candidate list including those from our previous system and a set of newly proposed features. In addition, through independent optimization of overlap exclusion and labeling, we obtain a relative diarization error rate improvement of 15.1% on a sampled subset of the AMI Meeting Corpus, more than double our previous result. When analyzed independently, we show that the performance improvement due to overlapped speech exclusion now rivals that of an oracle system using reference overlap segments.