Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods
Sound
2019-04-29 v1 Computation and Language
Audio and Speech Processing
Abstract
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.
Cite
@article{arxiv.1904.11641,
title = {Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods},
author = {Mohammed Senoussaoui and Patrick Cardinal and Najim Dehak and Alessandro Lameiras Koerich},
journal= {arXiv preprint arXiv:1904.11641},
year = {2019}
}