Evaluation of spherically invariant random process parameters as discriminators for speaker verification

In this work, we are interested in the potential use of spherically invariant random processes (SIRPs), described by two parameters, for speaker identification. These random processes have been shown to be a more statistically-accurate model for speech than Laplace and Gamma probability density functions. Computation of the two SIRP parameters is fast and simple and storage requirements are obviously small. Although the proposed method does not yield the accuracy of current methods, identification rates are better than random guessing. The work demonstrates the first step for potential use of SIRPs in speaker identification. Usage might include an adjunct role where SIRPs could supplement existing methods to further improve identification or be used to reduce the parameter requirements of existing methods while maintaining accuracy rates.