Automatic Text-Independent Artifact Detection, Localization, and Classification in the Synthetic Speech

Abstract

The paper describes experiments with statistical approaches to automatic detection, localization, and classification of the basic types of artifacts in the synthetic speech produced by the Czech text-to-speech system using the unit selection method. The first experiment is aimed at artifact detection by the analysis of variances (ANOVA) and hypothesis testing. The second experiment is focused on localization of the detected artifacts by the Gaussian mixture models (GMM). Finally, the developed open-set artifact classifier is described. The influence of the feature vector length and structure on the resulting artifact detection accuracy is analyzed together with other factors affecting the stability of the artifact detection process. Further investigations have shown a relatively great influence of the number of mixtures and the type of a covariance matrix on the artifact classification error rate as well as on the computational complexity. The obtained experimental results confirm the functionality of the artifact detector based on the ANOVA and hypothesis tests, and the GMM-based artifact localizer and classifier. The described statistical approaches represent the alternatives to the standard listening tests and the manual labeling of the artifacts.