Detection of Overlapping Speech for the Purposes of Speaker Diarization

Abstract

The presence of overlapping speech has a significant negative impact on the performance of speaker diarization systems. In this paper, we employ a convolutional neural network for the detection of such speech intervals and evaluate it in terms of the potential improvements to speaker diarization. We train the network on specifically-created synthetic data, while the evaluation is performed on the AMI Corpus and the SSPNet Conflict Corpus.