Abstract: This paper addresses the problem of speaker diarization in the specific context of meeting room recordings which often involve a high degree of spontaneous speech with large overlapped speech segments, speaker noise (laughs, whispers, coughs, etc.) and very short speaker turns. A large variability in signal quality has brought an additional level of complexity. This paper investigates the effects of speech activity detection and overlapped speech through speaker diarization experiments conducted on the NIST RT'05 and RT'06 data sets. Results indicate that our system is highly sensitive to the shape of the initial segmentation and that, perhaps surprisingly, perfect references can even degrade performance. Finally we propose a direction for future research to incorporate confidence values according to acoustic attributes in order to unify what is currently a somewhat disjointed approach to speaker diarization.

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