# Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods

## TL;DR

This paper introduces a novel approach for automatic speaker sincerity detection using covariance feature vectors and ensemble support vector regressors, achieving significant correlation improvements on a dedicated speech corpus.

## Contribution

It presents a new covariance-based feature representation combined with ensemble regression methods for speaker sincerity estimation, outperforming baseline systems.

## Key findings

- 8.1% relative improvement in Spearman's correlation
- Effective use of covariance features with acoustic sets
- Enhanced accuracy in sincerity degree estimation

## 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.