# Emotion Recognition from Speech in a Subject-Independent Approach
## 6 Results and Discussion
### Classification Emotions in Pairs  
The low accuracy of multiclass classification in the subject-independent setting can be partly attributed to the short duration of the analyzed segments--just one second. Within such a limited temporal context, it is typically possible to detect only a few strongly contrasting emotions (e.g., anger vs. neutrality). Therefore, in the next step, we examined which pairs of emotions are the easiest to distinguish. Results were presented for one-second windows and the RAVDESS and EMO-BAJKA dataset. A cubic SVM (for 96 features) was used for classification. The data was split according to the SS-70-10-20 strategy. Table 8 shows the results for classifying emotions in pairs for the RAVDESS. Only results with an accuracy and F-score score greater than 0.7 are displayed. As observed (Figure 3), the classification of the emotions Angry-Neutral and Angry-Sad achieves excellent results, with both accuracy and the F-score reaching 1, indicating a perfect distinction between these pairs of emotions by the model. High scores were also achieved for the pairs Disgust-Sad and Disgust-Neutral, suggesting effective classification of these emotions. Although the remaining pairs of emotions have lower scores, they also demonstrate the model's good distinguishing capabilities, especially in the cases of Angry-Happy and Angry-Surprise. Table 9, in turn, presents the results for pairs of emotions for the EMO-BAJKA dataset (also only in cases where accuracy and F-score were greater than 0.7). The results for the Joy-Neutral pair are particularly high, suggesting that the model performs well in recognizing differences between states of joy and neutrality (Figure 4). The Surprise-Neutral pair achieves slightly lower results but still remains quite high, indicating the model's effectiveness in distinguishing these emotional states. The Surprise-Fear and Neutral-Fear pairs also show high accuracy and F-scores, demonstrating that the model efficiently classifies and differentiates these emotions. These results highlight the potential of the applied model for precise recognition and classification of varied emotional states based on speech analysis. In the analysis of emotion in pairs for both databases (the RAVDESS and EMO-BAJKA), the obtained precision, sensitivity, and specificity scores indicate very good classification performance in many cases. High precision (0.95 for the Surprise-Fear and Neutral-Fear pairs in EMO-BAJKA) means the model rarely produces false alarms--when it assigns an emotion label, it is usually correct. Sensitivity, which in most cases is close to precision (Angry-Happy: 0.839), confirms that the model effectively detects instances of a given emotion. High specificity (0.958 for Disgust-Sad) indicates the model accurately identifies non-instances of a given class, resulting in few false positives. For most emotion pairs, the AUC exceeds 0.75 (Angry-Happy: 0.85), confirming good class separation. An exception is the Surprise-Neutral pair in EMO-BAJKA (AUC = 0.452),  
Figure 4: Accuracy for pairwise emotion classification for EMO-BAJKA database. Figure 3: Accuracy for pairwise emotion classification for the RAVDESS. where classification proved much more difficult--even though the accuracy was relatively good, the distribution of results was close to random. In summary, high values of precision, sensitivity, and specificity, together with sufficiently high AUC in most cases, indicate that the model not only classifies emotions correctly but also maintains a balance between avoiding false alarms and omissions--an aspect particularly important in practical applications. The presented results show that for the RAVDESS and EMO-BAJKA database, the best results were obtained for different pairs of emotions. This may indicate significant differences in the way emotions are expressed in Polish and English. Cultural and linguistic differences can affect voice modulation, intonation, or even the choice of words, which in turn can influence the perception and classification of emotions.