Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods
Table 2: Spearman’s correlation coefficient obtained with SVR on reduced feature vectors.
['Input space', 'CF vector', 'LOSO CV', 'Test set']
Given the extraction procedure of the CF features, the reduction of the dimensionality can be carried out in two different levels. First, we can reduce directly the dimensionality of the output space (i.e. the 12880-dimensional space of CF features), or instead, we can reduce the dimensionality of the input space (i.e. the D-dimensional space of MFCC features). Given the high dimensionality of the CF vectors and the lack of training data we have preferred to perform the dimensionality reduction in the input space using a Principal Component Analysis (PCA). In this case, only the SVR was used because it was the approach that provided the best results in our previous experiments.