# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 3 Experiments and Results
### Results
In the case of EmoDB, for both representations the classification was noticeably improved _fea_ emotional state. On the other hand, in both cases, accuracy for _sad_ remained unchanged. For RAVDESS, the highest, positive change was occurred in _dis_ for both representations. In both case, EmoDB and RAVDESS, for Mel spectrogram, accuracy for _ang_ remained unchanged but accuracy for _lap_ was decreased. For the models trained on CQT representation, for both databases, classification of _neu_ was also decreased. Figure 7: The classification accuracy relationship with the target feature space size and \(\beta\) coefficient for models without (**a**,**c**) and with (**b**,**d**) injection mechanism. The top row is for the EmoDB database, and the bottom is for the RAVDESS database. Figure 8: The confusion matrices for the best models trained on CQT representations without (**a**) and with injection mechanism (**b**)—EmoDB. Figure 10: The confusion matrices for the best models trained on CQT representations without (**a**) and with injection mechanism (**b**)—RAVDESS. Figure 9: The confusion matrices for the best models trained on Mel spectrograms without (**a**) and with injection mechanism (**b**)—EmoDB. Moreover, Figures 12 and 13 shows the classification distribution of individual emotional states of the models with the size of the final space feature \(M\), for which the highest average improvement was observed when using the injection mechanism. In the case of EmoDB, for CQT representation and Mel spectrogram, it was \(M=2048\) and \(M=1024\), respectively. For RAVDESS, it was \(M=1024\) for both representations. The ranges of some results have changed significantly in reduction. For CQT, these were _hap_ and _fea_, and _neu_ for Mel spectrogram. In both cases, the range of results for the _bor_ state has changed extension. Additionally, in Tables 7 and 8 average values from all models, without injection mechanism (\(WA\)) and with injection mechanism (\(WA_{1}\)), are presented for individual emotional states. Furthermore, for every emotion, the change in the classification accuracy after using the \(WA_{\mathbf{A}}\) injection mechanism was shown. Table 7 contains results for the EmoDB database. How can be noticed, in the case of CQT representation, the highest average improvement was recorded for emotions _hap_--14.6% and _neu_--6.5%. Classification of other emotional states also improved slightly in the range from 1.2% to 4%. In turn, for Mel spectrograms, the classification of _neu_ emotional state improved significantly 8.7% compared to other emotions, for which there was also an improvement, but to a lesser extent range 4.1-5.9%. The exception was _sad_ emotion, where the injection mechanism worsened the average classification level by 1%. The situation is different in the case of the RAVDESS database, which is shown in Table 8. The results for the CQT representation show significant improvement for emotions like _hap_--7.5% and _sad_--7.1%. However, a minimal \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & _neu_ & _ang_ & _fea_ & _hap_ & _sad_ & _dis_ & _cal_ & _sur_ \\ \hline Mel & 0 & 0 & 4.2 & \(-8.3\) & 0 & 12.5 & \(-4.2\) & 4.2 \\ CQT & \(-12.5\) & \(-8.3\) & 8.4 & 16.7 & \(-4.1\) & 16.7 & 12.5 & \(-4.1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Differences between classification accuracy \(WA\) of individual emotions with and without injection mechanisms for the best models trained on the RAVDESS database. Figure 11: The confusion matrices for the best models trained on Mel representations without (**a**) and with injection mechanism (**b**)—RAVDESS. deterioration of 0.1% _dis_ classification accuracy has been observed. For the remaining emotional states, a slight improvement occurred from 0.5% to 3.5%. For models trained on Mel spectrograms, significant deterioration was observed for _cal_--6.6%, the highest improvement occurred for _sad_ emotional state equal to 6.2%. For the rest of the emotions, accuracy slightly improved from 0.2% to 4.8%.