  Automatic measuring of speaker sincerity degree is a novel research problem
in computational paralinguistics. This paper proposes covariance-based feature
vectors to model speech and ensembles of support vector regressors to estimate
the degree of sincerity of a speaker. The elements of each covariance vector
are pairwise statistics between the short-term feature components. These
features are used alone as well as in combination with the ComParE acoustic
feature set. The experimental results on the development set of the Sincerity
Speech Corpus using a cross-validation procedure have shown an 8.1% relative
improvement in the Spearman's correlation coefficient over the baseline system.
