# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 3 Experiments and Results
### Results  
Figure 5 shows the distribution of classification results for the test set using all prepared models without injection mechanism, divided into the individual sizes of the target feature space \(M\). The graph on the left side (Figure 5a) shows the results in the form of \(WA\) obtained by the models trained on the EmoDB database, and the chart on the right side (Figure 5b) presents results for the models trained on the RAVDESS database. On the other hand, Figure 6 also illustrates the distribution of classification results in the same manner but obtained for models with injection mechanisms. Like Figure 5, the left graph (Figure 6a) presents results for the EmoDB database and the right (Figure 6b) for the RAVDESS database. As you can observe, in the general classification for both databases, models trained on CQT representations achieved significantly better classification accuracy for both variants, with and without injection mechanism, than models trained on Mel spectrograms. Additionally, in both cases, EmoDB and RAVDESS, these graphs illustrate the noticeable improvement in classification with the feature injection mechanisms for both CQT and Mel spectrogram. Moreover, in the case of EmoDB and models trained on CQT representation, the injection mechanism significantly reduced the range of results obtained by individual models within a given variant, which is especially visible for \(M=128\) and \(M=2048\). Furthermore, by comparing the highest accuracy obtained by the baseline models with the best result using the injection mechanism, an improvement of 3.9% for CQT and 6.25% for the Mel spectrogram was achieved for EmoDB. In the case of RAVDESS, it was 1.06% for the Mel spectrogram and 4.12% for CQT representation. Tables 2-4 show averaged results for individual base representations and sizes of the target feature space \(M\), as well as the subsequent values of \(\beta\). The results are presented as \(WA\) for the base models and \(WA_{I}\) for the injection mechanism models. In the case of the EmoDB database, the results are additionally presented in the form of \(UA\) for base models and \(UA_{I}\) for models with injection mechanisms. Additionally, for each measure, the value (\(WA_{\Delta}\), \(UA_{\Delta}\)) is shown by what improvement occurred after applying the feature injection mechanism for individual sizes \(M\). In the case of EmoDB, the highest average scores, 74.01% (WA) for the base models, were obtained for CQT representation and \(M=32\), while for the variant using the injection mechanism, was equal to 75.31% (WA) for \(M=128\), which gives an improvement of 1.3% (WA). For the same value \(M=128\), the improvement was 4.69% (WA). the overall highest result was obtained for the Mel spectrogram when injecting features, the average improvement was 8.75% (WA), where the injected features constituted 10% of the total. The lowest average difference was observed for \(M=512\), which was 0.87% (WA) for \(\beta=0.19\). For the RAVDES database, the highest average accuracy, 66.04% (WA), for the base models, was also achieved for CQT representation and \(M=32\). The variant with injection mechanism was 69.06% (WA) for \(M=256\), which gives an improvement equal to 3.02% (WA). For the same value \(M=256\), the improvement was 4.27% (WA). The best average improvement, 4.48% (WA), was achieved for \(M-1024\) where \(\beta\) was equal to 0.10. On the other hand, the lowest improvement 1.77% was observed for \(M=512\) and \(\beta=0.19\). In the case of base models trained on Mel spectrograms, the highest average result was 56.77% (WA), which, compared to the best score of 57.4% (WA) for the variant with injection mechanism, gives improvements equal to 0.63% (WA). The graphs in Figure 7 show the relationship of classification quality (\(WA\)) with the size of the target feature space. Figure 7a and 7c present information for models without injection mechanism for EmoDB and RAVDESS databases, respectively. Figure 7b,d illustrate results for models with injection mechanism also for EmoDB and RAVDESS.