# Emotion Recognition from Speech in a Subject-Independent Approach
## 6 Results and Discussion
### Feature Analysis and Their Impact on Classification Performance  
For the EMO-BAJKA dataset, using one-second windows with a 0.1-s overlap resulted in 2376 examples, each represented by 96 acoustic features. Similarly, for the RAVDESS, 3249 examples were obtained, also described using the same feature set. Although one might intuitively expect a high degree of redundancy among the extracted features, correlation analysis revealed that the vast majority of features were uncorrelated [72]. Only a few feature pairs showed high correlations above 0.95 but below 0.98. These included energy-related features: RMS and RMSsr, RMS and RMSmax, Esr and RMSmax, and Emax and RMSsr. This correlation stems from their similar physical properties--all of these features describe the intensity of the speech signal in the time domain. The remaining features showed only weak correlations or were completely independent, confirming their informational complementarity. This justifies the use of the full set of 96 features for the emotion classification task, as each contributes uniquely by capturing different aspects of the signal. However, in the course of the study, an attempt was made to reduce the number of input features by applying various statistical and heuristic selection methods, such as the \(t\)-test, ANOVA, the Kruskal-Wallis test, Relieff, and the SHAP (SHapley Additive exPlanations) method [73; 74]. The aim was to identify a minimal set of features that would maintain high accuracy in emotion classification. However, despite employing multiple approaches, every attempt to reduce the number of features--regardless of the selection method or the number of features chosen--resulted in decreased classification accuracy for both the RAVDESS and EMO-BAJKA dataset. Even for emotion pairs, it was challenging to find individual features that enabled effective class discrimination. In many cases, only the combination of several features allowed for partial separation of emotions. Figure 5 presents visualization results for the two features with the highest discriminative power, selected based on the Student's \(t\)-test, for the classes angry and neutral. The sample distribution indicates a certain degree of class separation: samples from the angry class form a more compact cluster within a specific area of the feature space, whereas samples from the neutral class are more dispersed and partially overlap with the opposite class. This indicates that even the best individual features do not guarantee complete separation of emotions, and effective classification requires the use of a larger set of features or models that take into account their interdependencies. In contrast to visualizations based on individual features, the use of Principal Component Analysis (PCA) enables the combination of information contained in all acoustic features. Figure 6 presents data for the angry and neutral classes after projecting them onto the space of the first two principal components. The resulting scatter plot indicates that this multidimensional transformation allows for clearer class separation, even though none of the individual features enables unambiguous emotion discrimination on its own. Thissuggests that most features provide important, complementary information that becomes useful for classification only when considered together. The result indicates that the features are mutually complementary, and the effectiveness of classifiers relies on leveraging the complex relationships among all available data dimensions. Even if individual features are not strongly discriminative by themselves, they can form significant combinations and contribute to the classifier's performance when used collectively. To gain a better understanding of the importance of individual features and their contribution to classification decisions, an additional feature importance analysis was conducted using the SHAP method, which is based on Shapley values from cooperative game theory.