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In this paper, we investigate the use of the mean shift algorithm with respect to speaker clustering. The algorithm is an elegant nonparametric technique that has become very popular in image segmentation, video tracking and other image processing and computer vision tasks. Its primary aim is to detect the modes of the underlying density and consequently merge those observations being attracted by each mode. Since the number of modes is not needed to be known beforehand, the algorithm seems to fit well to the problem of speaker clustering. However, the algorithm needs to be adapted; the original algorithm acts on the space of observations, while speaker clustering algorithms act on the space of probabilistic parametric models. We attempt to adapt the algorithm, based on some basic concepts of information geometry, that are related to the exponential family of distributions.

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