Experiment with GMM-Based Artefact Localization in Czech Synthetic Speech

The paper describes an experiment with using the statistical approach based on the Gaussian mixture models (GMM) for localization of artefacts in the synthetic speech produced by the Czech text-to-speech system employing the unit selection principle. In addition, the paper analyzes influence of different number of used GMM mixtures, and the influence of setting of the frame shift during the spectral feature analysis on the resulting artefact position accuracy. Obtained results of performed experiments confirm proper function of the chosen concept and the presented artefact position localizer can be used as an alternative to the standardly applied manual localization method.