In this paper, we present an open-set online speaker diarization system. The system is based on Gaussian mixture models (GMMs), which are used as speaker models. The system starts with just 3 such models (one each for both genders and one for non-speech) and creates models for individual speakers not till the speakers occur. As more and more speakers appear, more models are created. Our system implicitly performs audio segmentation, speech/non-speech classification, gender recognition and speaker identification. The system is tested with the HUB4-1996 radio broadcast news database.
Bibliographic reference. Geiger, Jürgen / Wallhoff, Frank / Rigoll, Gerhard (2010): "GMM-UBM based open-set online speaker diarization", In INTERSPEECH-2010, 2330-2333.