Robust Estimation for Sparse Data

Abstract

Robust parameters estimation of sparse data is generally applied to the test cases of time-consuming or high cost data collection. This study concerns with the problem in small sample size which is often encountered in the client data processing for speaker verification. We found that there always exists a coverage mismatch problem between the samples and its population in terms of probability density function (pdf) when the sample size is less than 20. We call this special problem the distribution mismatch (DM) problem. The paper proposes to solve the DM problem through addressing a new coverage-based estimator.