Automatic GMM-Based evaluation of noise suppression in the speech signal recorded during phonation in the open-air MRI

Abstract

The paper is focused at evaluation of successfulness of de-noising of the speech signal recorded in the open-air magnetic resonance imager during phonation for the 3D human vocal tract modeling. Automatic evaluation methods based on classification by Gaussian mixture models (GMM) are described in more detail. The first performed experiments have confirmed that the proposed GMM classifier of the speech quality is functional and fully comparable with the standard evaluation based on the listening test. Our investigations have shown a relatively great influence of the number of mixtures and the type of a covariance matrix on the computational complexity. However, there was less effect of these parameters on variability of the GMM classifier results for different de-noising methods as well as less effect on gender invariability of the results.