Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods
This work addresses speaker sincerity detection for computational paralinguistics, representing an incremental advance in the field.
The paper tackled the problem of automatically measuring speaker sincerity by proposing covariance-based feature vectors and ensemble methods, achieving an 8.1% relative improvement in Spearman's correlation coefficient over the baseline on the Sincerity Speech Corpus.
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.