# Emotion Recognition from Speech in a Subject-Independent Approach
## 6 Results and Discussion
### Classification Accuracy for SS-70-10-20 Strategy and One-Second Speech Segments with 0.1-s Overlap
Therefore, it can be stated that using deep learning, such as LSTM or CNNs, matches or even exceeds human emotion recognition. Figure 2 presents the ROC curves for individual emotion classes from the RAVDESS, obtained using a CNN classifier with 96 features. These curves illustrate the effectiveness of distinguishing each emotion in a one-vs.-all manner. The shape of the curves and the corresponding AUC values indicate varying classification performance across the different classes. The highest AUC values were achieved for the emotions angry and calm (both 0.84), indicating good separability of these classes from the others. The emotion happy was also recognized with high accuracy (AUC = 0.82). Slightly lower, yet still respectable, results were observed for the classes fearful (0.76), sad (0.74), and surprised (0.72). The model performed noticeably worse for the emotion disgust (AUC = 0.62), suggesting difficulty in distinguishing this emotion from other affective states. The weakest performance was recorded for the neutral class (AUC = 0.55), indicating that the model struggles to differentiate neutral utterances from emotional ones. The average AUC, calculated as the macro-average, was 0.73, reflecting a moderate overall effectiveness of the classifier in the multi-class emotion recognition task. Table 6 shows an example confusion matrix obtained for the CNN method (96 features) for classifying eight emotions (RAVDESS). The classified emotions are anger, calm, disgust, fear, happiness, neutrality, sadness, and surprise. Each row represents the actual emotions, and each column represents the emotions predicted by the model. The values in the cells denote the number of cases. In turn, Table 7 presents metrics such as precision, recall, specificity, accuracy, F-score, and AUC calculated for classes (1-8) and the CNN classifier for the RAVDESS. The analysis of the classification results for the eight emotions offers insight into the model's capability to distinguish between various emotional states. The emotion classification performance of a CNN model varies significantly depending on the emotion class. Precision varies among the emotion categories, with the highest value for anger (0.78) and the lowest for disgust (0.24) and fear (0.25). The average precision (macroAVG) is 0.36, indicating the model's moderate ability to correctly identify examples. Sensitivity suggests that the model is best at recognizing calm (0.78) but struggles with identifying fear (0.03) and neutrality (0.13), with an average sensitivity of 0.32. Specificity is relatively high for all emotions, with the lowest value for calm (0.75) and the highest for fear  
(0.99), averaging 0.90. The accuracy is consistent across all emotions at 0.34, reflecting the overall ability of the model to correctly classify examples. The F-score, the harmonic mean of precision and sensitivity, also shows variation, with the best result for anger (0.60) and the worst for fear (0.06). The average F-score is 0.30, highlighting the challenges associated with balancing precision and sensitivity in emotion recognition. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Angry** & **Calm** & **Disgust** & **Fearful** & **Happy** & **Neutral** & **Sad** & **Surprise** \\ \hline Angry & 49 & 21 & 10 & 3 & 14 & 8 & 11 & 9 \\ \hline Calm & 22 & 78 & 18 & 2 & 25 & 6 & 20 & 7 \\ \hline Disgust & 11 & 25 & 31 & 1 & 18 & 5 & 10 & 5 \\ \hline Fearful & 6 & 2 & 1 & 3 & 2 & 1 & 1 & 1 \\ \hline Happy & 15 & 28 & 21 & 2 & 39 & 8 & 17 & 10 \\ \hline Neutral & 9 & 5 & 4 & 1 & 6 & 13 & 5 & 3 \\ \hline Sad & 13 & 22 & 11 & 1 & 19 & 7 & 31 & 8 \\ \hline Surprise & 10 & 8 & 5 & 1 & 10 & 3 & 9 & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix for CNN (96 features) for RAVDESS.