Emotion recognition from speech using Fisher's discriminant analysis and Bayesian classifier

In this study, a large number of features that were obtained to classify speech emotions were projected into different spaces, selecting different numbers of principal components in principal component analysis and Fisher's discriminant analysis. Classifications were performed in those spaces using Naïve-Bayes classifier and obtained results were compared. While the highest accuracy obtained in the Fisher space was 57.87%, it was calculated as 48.02% in the principal component space.