A method for computing a distance between collections of distributions or
finite mixture models of features. Data is processed so as to define at
least first and second collections of distributions of features. For each
distribution of the first collection, the distance to each distribution of
the second collection is measured to determine which distribution of the
second collection is the closest (most similar). The same procedure is
performed for the distributions of the second collection. Based on the
closest distance measures, a final distance is computed representing the
distance between the first and second collections. This final distance may
be a weighted sum of the closest distances. The distance measure may be
used in a number of applications such as [speaker classification,] speaker
recognition and audio segmentation.
Other References
Thomas E. Flick, et al. "A Minimax Approach to Development of Robust
Discrimination Algorithms for Multivariate Mixture Distributions," Proc.
IEEE ICASSP 88, vol. 2, pp. 1264-1267, Apr. 1988.*
Homayoon sadr Mohammad Beigi, et al. "A Distance Measure Between
Collections of Distributions and its Application to Speaker Recognition,"
Proc. IEEE ICASSP 98, vol. 2, pp. 753-756, May 1998.*
Geoff A. Jarrad, et al. "Shared Mixture Distributions and Shared Mixture
Classifiers," Proc. IEEE IDC 99, pp. 335-340, Feb. 1999.
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