## 5 Results
### Speech Emotion Recognition Results  
The results of Table 1 summarize the performance of the speech emotion recognition models tested. For the feature extraction strategy using the 512-dimensional representation obtained from the feature encoder of the xlsr-Wav2 Vec2.0, we reached the maximum accuracy when we passed them to the MLP of one layer. This model outperformed all the k-NNs tested in a statistically significant manner. Additionally, the MLP reached a higher average accuracy than the SVCs. Although the embeddings extracted from the feature encoder improved the accuracy of the ZeroR, demonstrating there was relevant information embedded in these representations, the Fine-Tuning strategy beat all the feature extraction-based methods. This fact demonstrated the plasticity of the xlsr-Wav2 Vec2.0 transformer to adapt its weights with new information. The fine-tuning version surpassed the top MLP in 25.29% points. This rise also suggests that our dataset has enough size to let the networks learn effectively from the recordings. Furthermore, the pre-trained weights contained consistent and compatible knowledge to solve the speech emotion recognition task. To understand the errors of the top solution, we extracted the confusion matrix from the predictions of the fine-tuned xlsr-Wav2 Vec2.0 that reached an accuracy of 81.82%. The confusion matrix displayed in Figure 5 is the rounded average value of the errors and the predictions obtained in the 5-CV. For this reason, this matrix has 288 samples in total (1440/5). Figure 5 reveals that the model exhibited good performance for most samples except for some cases. For example, the 'Neutral' and 'Calm' classes were sometimes confused due to the similarities between these two emotions. Likewise, the 'Sad' class was occasionally mistaken with 'Fearful', 'Calm', or 'Neutral'. The errors between 'Sad' and 'Fearful' were understandable because of the low arousal present in both emotions. On the other hand, the mistakes between the predictions of 'Sad', 'Neutral', or 'Calm' could come from the neutral voice chunks at the beginning of some recordings that may have create the false perception that they belonged to 'Calm' or 'Neutral' classes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**TL Strategy** & **Inputs** & **Models** & \begin{tabular}{c} **Hyper-** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Accuracy \(\pm\)** \\ **95\% CI** \\ \end{tabular} \\ \hline - & - & \begin{tabular}{c} Human \\ perception \\ \end{tabular} & - & 67.00 \\ \hline - & - & ZeroR & - & 13.33 \(\pm\) 1.76 \\ \hline \multirow{5}{*}{\begin{tabular}{c} Feature \\ Extraction \\ (Static) \\ \end{tabular} } & \multirow{5}{*}{\begin{tabular}{c} Average \\ xlsr-Wav2 Vec2.0 \\ embs. from \\ feature encoder \\ \end{tabular} } & \multirow{5}{*}{\begin{tabular}{c} KNN \\ \end{tabular} } & C = 1.0 & 50.13 \(\pm\) 2.58 \\ \cline{3-4}  & & & C = 100.0 & 53.12 \(\pm\) 2.58 \\ \cline{3-4}  & & & C = 100.0 & 53.10 \(\pm\) 2.58 \\ \cline{3-4}  & & & K = 10 & 36.07 \(\pm\) 2.48 \\ \cline{3-4}  & & & k = 20 & 37.90 \(\pm\) 2.51 \\ \cline{3-4}  & & & k = 30 & 38.65 \(\pm\) 2.51 \\ \cline{3-4}  & & & k = 40 & 38.47 \(\pm\) 2.51 \\ \cline{3-4}  & & & MLP & 1 layer (80) & **56.53 \(\pm\) 2.56** \\ \cline{3-4}  & & & 2 layers (80,80) & 55.82 \(\pm\) 2.56 \\ \hline \multicolumn{2}{c}{\begin{tabular}{c} Fine \\ Tuning \\ (Sequential) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Raw audio \\ + MLP \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} xlsr-Wav2 Vec2.0 \\ + MLP \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} MLP 2 layers \\ (1024,8) \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **81.82 \(\pm\) 1.99** \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative evaluation of the different strategies on speech emotion recognition. In bold, the best models per TL strategy. Figure 5: Average confusion matrix of the fine-tuned xlsr-Wav2 Vec2.0 experiment with an accuracy of 81.82%.