  Speaker diarization is an important pre-processing step for many speech
applications, and it aims to solve the "who spoke when" problem. Although the
standard diarization systems can achieve satisfactory results in various
scenarios, they are composed of several independently-optimized modules and
cannot deal with the overlapped speech. In this paper, we propose a novel
speaker diarization method: Region Proposal Network based Speaker Diarization
(RPNSD). In this method, a neural network generates overlapped speech segment
proposals, and compute their speaker embeddings at the same time. Compared with
standard diarization systems, RPNSD has a shorter pipeline and can handle the
overlapped speech. Experimental results on three diarization datasets reveal
that RPNSD achieves remarkable improvements over the state-of-the-art x-vector
baseline.
