# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 4 Conclusions  
We have proposed a simple approach to fuse the embedded space of convolutional neural networks and low-level features to obtain feature space with higher discriminative power for emotion recognition in speech signals. The experiments showed that source representation in CQT spectrograms achieves better classification results than popular Mel spectrograms for emotional speech. The results show that the models that used additional information from low-level features achieved better performance in the overall classification. The average improvement for the models trained on CQT representations was equal to 1.29% and 3.75% (weighted accuracy) for models trained on Mel spectrograms in the case of the EmoDB dataset. However, for RAVDESS, it was 3.02% and 0.63% for CQT representation and Mel spectrogram, respectively. Furthermore, for variants with the highest change in classification score, the results demonstrate that the average classification performance was improved for almost all emotions compared to variants without an injection mechanism for both databases. In the case of EmoDB, for best models where Mel spectrograms were base representations, the accuracy of all emotions was improved. The highest improvement, around 14%, was obtained for _sad_ and _dis_ states, and the lowest, around 5%, for _ang_, _hap_, and _bor_ emotional states. Using models trained on CQT representations, the most significant improvement in classification occurs for _hap_ and _dis_ (more than 20%) emotional states. On the other hand, the classification accuracy of anger and boredom decreases states by around 2%. For models that achieved the best accuracy on the RAVDESS database, the classification of _neu_ was improved by around 16% using the Mel spectrogram, and in the case of CQT representations, the classification of emotional states like _sur_ and _fa_ was increased by around 9%. In most cases, the resulting average classification accuracy was improved for individual emotional states. The most positive impact after applying the injection mechanism was observed for _hap_--14.6% and _neu_--8.7% emotions for the EmoDB \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CQT Spectrogram**} & \multicolumn{3}{c}{**Mel Spectrogram**} \\ \cline{2-7}  & \(WA\) & \(WA_{I}\) & \(WA_{\Delta}\) & \(WA\) & \(WA_{I}\) & \(WA_{\Delta}\) \\ \hline _neu_ & 64.0 & 70.5 & 6.5 & 48.6 & 57.2 & 8.7 \\ _ang_ & 91.2 & 92.4 & 1.2 & 78.3 & 81.7 & 3.5 \\ _fea_ & 47.1 & 49.3 & 2.2 & 35.4 & 41.3 & 5.9 \\ _hap_ & 49.9 & 64.5 & 14.6 & 41.3 & 46.9 & 5.6 \\ _sad_ & 88.6 & 90.7 & 2.1 & 88.8 & 87.7 & \(-1.0\) \\ _dis_ & 50.0 & 54.0 & 4.0 & 51.1 & 55.2 & 4.1 \\ _bor_ & 77.9 & 79.6 & 1.6 & 65.7 & 70.7 & 5.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average accuracy of all models without \(WA\) and with \(WA_{I}\) injection mechanism and their changes \(WA_{\Delta}\) for individual emotional states—EmoDB. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CQT Spectrogram**} & \multicolumn{3}{c}{**Mel Spectrogram**} \\ \cline{2-7}  & \(WA\) & \(WA_{I}\) & \(WA_{\Delta}\) & \(WA\) & \(WA_{I}\) & \(WA_{\Delta}\) \\ \hline _neu_ & 76.8 & 77.3 & 0.5 & 77.0 & 81.8 & 4.8 \\ _ang_ & 77.3 & 78.7 & 1.4 & 62.1 & 65.2 & 3.0 \\ _fea_ & 61.8 & 63.8 & 2.1 & 46.7 & 46.8 & 0.2 \\ _hap_ & 37.9 & 45.3 & 7.5 & 25.2 & 26.3 & 1.1 \\ _sad_ & 49.8 & 56.8 & 7.1 & 40.6 & 46.8 & 6.2 \\ _dis_ & 57.7 & 57.7 & \(-0.1\) & 41.4 & 43.2 & 1.8 \\ _cal_ & 71.7 & 75.2 & 3.5 & 76.1 & 69.5 & \(-6.6\) \\ _sur_ & 81.2 & 83.7 & 2.5 & 69.8 & 72.3 & 2.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average accuracy of all models without \(WA\) and with \(WA_{I}\) injection mechanism and their changes \(WA_{\Delta}\) for individual emotional states—RAVDESS. database. However, for the RAVDESS dataset, the most significant improvement equal to 7.5% and 7.1% occurred for _hap_ and _sad_ emotional states. Additionally, the classification of _sad_ emotion for EmoDB combined with Mel spectrogram has deteriorated by 1%.