# 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  
The SVM, LSTM, and CNN classification methods were evaluated on one-second audio signal fragments. An overlap of 0.1 s between recording segments was applied, slightly improving the utilization of the audio material by increasing the number of generated examples. We employ the SS-70-10-20 strategy for dividing data into training, validation, and testing sets. This approach facilitates the evaluation of the models' ability to generalize to unknown data, crucial for practical applications in real-world scenarios. The classification accuracy of emotions for the RAVDESS and EMO-BAJKA database is presented in Table 5. Analyzing the obtained results, several interesting trends can be observed, and conclusions can be drawn about the effectiveness of individual approaches. Based on the presented data, it is observable that none of the tested classification methods achieved satisfactory results in recognizing eight classes of emotions for the RAVDESS and seven classes of emotions for the EMO-BAJKA database. The best results for the EMO-BAJKA database were obtained using the CNN method with a 24-band mel spectrogram, achieving a classification accuracy of 0.213. Although this result is significantly better than those of other tested methods on the same database, it still remains \begin{table}
\begin{tabular}{c c c} \hline \hline
**Testing Strategy** & **RAVNESS** & **EMO-BAJKA** \\ \hline SS-70-10-20 & 0.362 & 0.152 \\ \hline RS-70-10-20 & 0.934 & 0.918 \\  & 0.932 & 0.946 \\  & 0.925 & 0.947 \\ \hline
5-CV & 0.947 & 0.949 \\  & 0.949 & 0.955 \\  & 0.945 & 0.952 \\ \hline
10-CV & 0.958 & 0.966 \\  & 0.959 & 0.965 \\  & 0.960 & 0.962 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy results for the SVM classifier with cubic kernel for one-second windows moved every 0.1 s. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **EMO-BAJKA** & **RAVNESS** \\ \hline SVM cubic (96 features) & 0.152 & 0.362 \\ \hline CNN (96 features) & 0.148 & 0.338 \\ \hline LSTM (mfcc 12 + pitch) & 0.140 & 0.325 \\ \hline CNN (melSpec 24 bands) & 0.213 & 0.324 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of emotion classification for the RAVNESS and EMO-BAJKA database using various classification methods. at a low level, indicating the difficulty of emotion classification tasks based on speech data for this specific database. For the RAVDESS, the best results were achieved using the cubic SVM method, with a classification accuracy of 0.362. This result is slightly better than those of other methods tested on this database, but it is also not considered satisfactory in the context of recognizing eight different emotion classes. This suggests that traditional classification methods, such as SVMs, may offer some advantages compared to more advanced techniques for specific datasets, yet their overall effectiveness level remains low. These results highlight the challenges facing researchers in the field of automatic emotion recognition in speech. The obtained results underscore the importance of selecting appropriate features and models for a specific emotion classification task. They also show that the same methods can perform with varying effectiveness across different datasets, which indicates the necessity of tailoring the approach to the characteristics of the data. Advanced techniques, such as neural networks (CNNs, LSTM), which can automatically extract and combine features from data, can potentially offer better results than traditional classification methods, especially when they are adapted to the data's specificity through the selection of suitable features and architecture. Emotion recognition by humans based on one-second clips indicates a classification accuracy of 0.40 (with a standard deviation of 0.18) for the EMO-BAJKA database and 0.28 (with a standard deviation of 0.14) for the RAVDESS.