Exponent parameter estimation for generalized Gaussian probability density functions with application to speech modeling

In this contribution, we exploit entropy matching to estimate the exponent parameter of a generalized Gaussian density. Based on this premise, we derive a new entropic expression with respect to higher-order moments of the modeled data, which yields a novel generalized source entropy matching estimator (G-EME). A number of other popular statistical methods are also reviewed, described and compared against the proposed technique. Extensive comparative experimental results illustrate the high accuracy of the proposed estimator, for both light- and heavy-tailed distributions, as well as speech data.