A distance measure between collections of distributions and its application to speaker recognition

This paper presents a distance measure for evaluating the closeness of two sets of distributions. The job of finding the distance between two distributions has been addressed with many solutions present in the literature. To cluster speakers using the pre-computed models of their speech, a need arises for computing a distance between these models which are normally built of a collection of distributions such as Gaussians (e.g., comparison between two HMM models). The definition of this distance measure creates many possibilities for speaker verification, speaker adaptation, speaker segmentation and many other related applications. A distance measure is presented for evaluating the closeness of a collection of distributions with centralized atoms such as Gaussians (but not limited to Gaussians). Several applications including some in speaker recognition with some results are presented using this distance measure.