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Automatic measuring of speaker sincerity degree is a novel research problemin computational paralinguistics. This paper proposes covariance-based featurevectors to model speech and ensembles of support vector regressors to estimatethe degree of sincerity of a speaker. The elements of each covariance vectorare pairwise statistics between the short-term feature components. Thesefeatures are used alone as well as in combination with the ComParE acousticfeature set. The experimental results on the development set of the SinceritySpeech Corpus using a cross-validation procedure have shown an 8.1% relativeimprovement in the Spearman's correlation coefficient over the baseline system.

Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods