This paper provides a description of the properties of the symmetric sphericity measure between two Gaussian classes, and presents experimental results of its use in the context of text independent speaker verification, performed on cellular speech. A novel geometric interpretation of the sphericity measure is presented, emphasizing its robustness to variations in scaling in feature space compared to other distortion measures, along with an associated score normalization procedure. The experimental results clearly indicate the superiority of this method over the prevailing likelihood ratio approach, motivating further use of the sphericity measure in a multi-component modeling scheme. In addition, the direct use of this method with single Gaussian models is computationally extremely simple, and may provide acceptable performance in certain cases.
['Ran D. Zilca', 'Upendra V. Chaudhari', 'Ganesh N. Ramaswamy']
The sphericity measure for cellular speaker verification