## 4 Results
### Comparison with Other Approaches
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Dataset**} & **Number** & **Best Performance in the Studied** & **Best Achieved Performance in Current Work** & **Description of the Classification Approach** \\  & **of Classes** & **Literature (Average Accuracy [\%])** & **(Average Accuracy [\%])** & **(a) In the Literature** \\ \hline \multirow{3}{*}{SAVEE} & \multirow{3}{*}{7} & \multirow{3}{*}{74\% [62]} & \multirow{3}{*}{78.81\%} & (a) LSTM-based network with attention mechanism; (b) 1D CNN with four convolutional layers and one fully connected layer, without batch normalization. \\ \cline{2-4}  & & & & (a) No similar work has been found; (b) 1D CNN with four convolutional layers, two GRU layers, batch normalization after each layer, and one fully connected layer. \\ \hline \multirow{3}{*}{RAVDESS} & \multirow{3}{*}{8} & \multirow{3}{*}{**84.1\% [63]**} & \multirow{3}{*}{79.62\%} & (a) Masked autoencoder; (b) 1D CNN with four convolutional layers and one fully connected layer, without batch normalization. \\ \cline{2-4}  & & & & (a) No similar work has been found; (b) 1D CNN with four convolutional layers, two GRU layers, with batch normalization after each layer, and one fully connected layer. \\ \hline \multirow{3}{*}{TESS} & \multirow{3}{*}{7} & \multirow{3}{*}{97.1\% [58]} & \multirow{3}{*}{**99.58\%**} & (a) 1D CNN; (b) 1D CNN with four convolutional layers and one fully connected layer, with batch normalization after each layer. \\ \cline{2-4}  & & & & (a) No similar work has been found; (b) CNN LSTM with four convolutional layers, two bidirectional LSTM layers, with batch normalization after each layer, and one fully connected layer. \\ \hline \multirow{3}{*}{CREMA-D} & \multirow{3}{*}{6} & \multirow{3}{*}{**82.96\% [60]**} & \multirow{3}{*}{69.18\%} & (a) Transformer based; (b) 1D CNN with four convolutional layers and one fully connected layer, with batch normalization after each layer. \\ \cline{2-4}  & & & & (a) No similar work has been found; (b) 1D CNN with four convolutional layers, two GRU layers, with batch normalization after each layer, and one fully connected layer. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracies achieved using different approaches on the 4 original datasets. For each dataset, we present the results achieved on the original dataset (with the original number of classes) and for a version of the dataset where the data was aggregated into the three classes of interest in this study: positive, neutral, and negative. Further details on the methods from the literature that achieved the best performance for each dataset are provided in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Emotions**} & \
\begin{tabular}{c} **Best Performance** \\ **(Average Accuracy [\%])** \\ \end{tabular} &
\begin{tabular}{c} **Description of the Model Architecture and** \\ **Parameters for Best Result** \\ \end{tabular} \\ \hline \multirow{4}{*}{Aggregated dataset} & \multirow{3}{*}{Seven emotions} & \multirow{3}{*}{71.77\%} & \(1\)D CNN with four convolutional layers and one fully connected layer, with batch normalization. \\ \cline{2-4}  & & & & \\ \cline{1-1} \cline{2-4}  & & & **CNN LSTM with four convolutional layers, two bidirectional LSTM layers and two fully connected layers, with batch normalization.** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Best accuracies achieved using different approaches on the aggregated dataset with the original seven detailed labels (happy, surprise, calm, angry, fear, sad, and neutral) and with labels aggregated in the three classes of interest (positive, neutral, and negative).