## 6 Results
### Speech LSTM Models Based on RAVDESS Dataset  
The first stage of research on speech emotion recognition models was to select a group of neural network models that gave the best results in comparison. At first glance, the group of LSTM models stood out: Stacked Time Distributed 2D CNN-LSTM, Stacked Time Distributed 2D CNN-Bidirectional LSTM with Attention, Parallel 2D CNN-Bidirectional LSTM with Attention, and Parallel 2D CNN-Transformer Encoder. The accuracy of these models, according to the authors, oscillated around 90-97% accuracy, which in comparison to other models gave a gain in accuracy of around 20%. This result allowed us to state that this is an ideal solution. Therefore, it was decided to confirm the given results by re-training the proposed LSTM model architectures on the RAVDESS database. These data \begin{table}
\begin{tabular}{c c} \hline
**Emotion** & **Number of Model Predictions** \\ \hline Surprise & 96 \\ Disgust & 0 \\ Anger & 0 \\ Fear & 6 \\ Sadness & 6 \\ Happiness & 0 \\ Neutral & 432 \\ \hline \end{tabular}
\end{table} 
Table 3: Evaluation of a text emotion recognition model on the nEMO dataset with neutral texts. allow for the classification of the following emotions: angry, calm, disgust, fearful, happy, sad, surprise, and neutral. According to the assumptions, the set was divided into training, validation, and test sets in the proportions of 80:10:10, then Mel spectrograms containing the appropriate time window of the spoken text were prepared and subjected to appropriate preprocessing methods. Models were trained on the prepared data and tested, taking into account the most important metrics for classification algorithms. Accuracy reflects the proportion of correct predictions out of all predictions made by a model, whereas precision focuses on the ratio of true positives to all positive predictions. Recall measures the model's ability to identify all actual positive cases, and the F1 score combines precision and recall into a single metric, giving a balanced measure of performance when dealing with imbalanced data. The results of the mentioned metrics for each category and the average value are summarized in Table 4. For each emotion and the average value, the best results are bolded. At first glance, the difference between the evaluation of the authors of the LSTM models and the retraining is visible. The most accurate model is the Parallel 2D CNN-Bidirectional LSTM with Attention with an accuracy of 65%, which gives a result worse by several percent than other models proposed in comparison with a similar number of categories. The metrics for individual emotions are mostly the best for the aforementioned model. However, two other models cannot be rejected-Parallel 2D CNN-Transformer Encoder and Stacked Time Distributed 2D CNN-Bidirectional LSTM, whose accuracy is several percent lower. For a better analysis of individual emotions, it was decided to present a confusion matrix for each model. A confusion matrix is a summary table used to evaluate the performance of a classification model by displaying the counts of true positives, true negatives, false positives, and false negatives. It offers insights into the model's ability to correctly predict each class, helping identify areas where the model may be biased or making errors. The confusion matrices for the evaluated models are presented in Figure 3.  
Confusion matrices are clearly diagonal, which allows us to claim that the predicted values match the true emotions. From the evaluation of metrics, it was possible to notice a poor accuracy of the models in the case of the neutral state, which is confirmed in the above matrices. This results from the small sample of the neutral category in the set. For the most accurate model, the number of well-recognized categories for each emotion is distributed evenly, which indicates that the model best balances the prediction results despite the low accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Models**} & \multicolumn{1}{c}{\