on-line multi-modal speaker diarization
This paper presents a novel framework that utilizes multi-modal information to achieve speaker diarization. We use dynamic Bayesian networks to achieve on-line results. We progress from a simple observation model to a complex multi-modal one as more data becomes available. We present an efficient way to guide the learning procedure of the complex model using the early results achieved with the simple model. We present the results achieved in various real-world situations, including videos coming from webcameras, human computer interaction and video conferences.
multi-modal speaker diarization speaker detection audio-visual