# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 3 Experiments and Results
### Results
Moreover, comparing graphs contained results for EmoDB, the use of the injection mechanism not only had a positive impact on the average classification result but also reduced the average spread of results obtained for different values of \(M\) parameter. Additionally, the Figure 7b shows the \(\beta\) coefficient changes depending on the \(M\) value. Moreover, changes in the \(\beta\) coefficient do not significantly impact the average level of classification, which remains at a more or less similar level throughout the entire range of \(\beta\) changes. Such a situation and the average improvement in results after using the injection mechanism may prove the importance of the injected features, which partially reduces the complexity of the neural network architecture while maintaining its level of prediction. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**CQT Spectrogram**} & \multicolumn{3}{c}{**Mel Spectrogram**} \\ \cline{2-9} \(M\) & \(WA\) & \(WA_{I}\) & \(WA_{\Delta}\) & \(WA\) & \(WA_{I}\) & \(WA_{\Delta}\) & \(\beta\) \\ \hline
32 & 66.04 & - & - & 56.25 & - & - & - \\
64 & 63.85 & - & - & 54.58 & - & - & - \\
128 & 64.06 & 66.49 & 2.43 & 53.96 & 55.10 & 1.14 & 0.77 \\
256 & 64.79 & 69.06 & 4.27 & 54.27 & 56.35 & 2.08 & 0.39 \\
512 & 63.44 & 65.21 & 1.77 & 56.77 & 56.67 & \(-\)0.1 & 0.19 \\
1024 & 62.71 & 67.19 & 4.48 & 53.85 & 57.40 & 3.55 & 0.10 \\
2048 & 64.90 & 68.65 & 3.75 & 54.37 & 56.98 & 2.61 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean values for \(WA\), and their changes \(WA_{\Delta}\), for each \(M\) and \(\beta\) values (CQT and Mel spectrogram)—RAVDESS. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \(M\) & \(WA\) & \(UA\) & \(\beta\) & \(WA_{I}\) & \(UA_{I}\) & \(WA_{\Delta}\) & \(UA_{\Delta}\) \\ \hline
32 & 60.78 & 59.52 & - & - & - & - & - \\
64 & 62.19 & 61.95 & - & - & - & - & - \\
128 & 58.44 & 56.91 & 0.77 & 64.69 & 62.69 & 6.24 & 5.78 \\
256 & 58.44 & 56.48 & 0.39 & 65.02 & 62.89 & 6.58 & 6.41 \\
512 & 62.5 & 61.15 & 0.19 & 63.75 & 62.02 & 1.24 & 0.87 \\
1024 & 58.28 & 56.73 & 0.10 & 66.25 & 65.48 & 7.82 & 8.75 \\
2048 & 59.22 & 56.39 & 0.05 & 63.28 & 61.79 & 4.06 & 5.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean values for \(UA\), \(WA\), and their changes \(WA_{\Delta}\), \(UA_{\Delta}\) for each \(M\) and \(\beta\) (Mel spectrogram)—EmoDB. Figure 8 and Figure 9 show the confusion matrices of models trained on EmoDB, without and with the mechanism of injection that received the highest scores for CQT and Mel spectrograms, respectively. Similarly, Figures 10 and 11 illustrate confusion matrices for RAVDESS database. Tables 5 and 6 present a classification accuracy improvement in individual emotional states for the models depicted on confusion matrices. In the case of EmoDB, injection of additional features (\(\beta=0.05\)) for CQT representation significantly improved classification accuracy for _dis_ emotional state, 30% (WA). A slight improvement was also observed for the _fea_ and _ang_ states, which were equal to 16.7% (WA) and 6.7% (WA), respectively. The classification accuracy of emotions such as _neu_ and _bor_ was decreased. However, the situation for _lap_ and _sad_ did not change. A different result occurred in the case of the Mel spectrogram (\(\beta=0.1\)), where only _lap_ got worse by 16.6% (WA). However, significant improvement was observed for _neu_ equal to 21% (WA) and for _fea_ equal to 38.9% (WA). No changes for _ang_, _sad_, _dis_ and _bor_. A different situation can be observed in the results for the RAVDESS database. The most significant improvement occurred for _dis_--12.5% (WA) in the case of Mel spectrogram and for _lap_, _dis_ equal to 16.7% (WA) in the case of CQT representation. On the other hand, the accuracy of classification was decreased in the model trained on Mel spectrograms for _lap_ and _cal_. For the CQT representations, the classification of _neu_, _ang_, _sad_, and _sur_ has deteriorated.