# Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network
## 4 Experimental Results and Discussion
### Summary  
In this study, the MFCCT features were proposed using a 1D CNN model for an effective SER, utilizing the EMO-DB, SAVEE, and RAVDESS datasets. The results revealed that the MFCCT feature coupled with 1D CNN can efficiently identify emotions from speech. Furthermore, the results revealed that the MFCCT features, utilized as input to 1D CNN to retrieve the high-level patterns, are more discriminative at obtaining enhanced performance of the SER system. Several research studies have proposed various methods for SER employing DL models and handcrafted features, but these methods are still not effective in terms of size, computational time, and recognition accuracy [42; 43]. This research adopted  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No of \(t\)-Domain** & & & & \\
**Features** & & & & \\ \hline
2 & 81.6 & 53.8 & 48.2 \\
4 & 84.6 & 73.6 & 69.3 \\
6 & 87.4 & 78.5 & 73.9 \\
8 & 88.3 & 80.1 & 76.8 \\
10 & 90.6 & 83.7 & 79.8 \\
**12** & **96.6\%** & **92.6\%** & **91.4\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: t-domain features used to obtain MFCCT features.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Datasets**} & \multicolumn{8}{c}{**Accuracy (x100\%) of Individual Emotion**} \\ \cline{3-11}  & & **A** & **B** & **C** & **D** & **F** & **H** & **N** & **S** & **U** & **Avg** \\ \hline \multirow{2}{*}{[20]} & EMO-DB & 1.00 & 0.61 & \(\times\) & 0.67 & 0.67 & 1.00 & 1.00 & 0.87 & \(\times\) & 0.86 \\  & RAVDESS & 0.92 & \(\times\) & 0.57 & 0.72 & 0.76 & 0.68 & 0.75 & 0.52 & 0.80 & 0.71 \\ \hline \multirow{2}{*}{[41]} & EMO-DB & 0.92 & 0.88 & \(\times\) & 0.99 & 0.92 & 0.92 & 0.90 & 0.93 & \(\times\) & 0.90 \\  & RAVDESS & 0.80 & \(\times\) & 0.90 & 0.71 & 0.74 & 0.65 & 0.68 & 0.66 & 0.67 & 0.73 \\  & SAVEE & 0.90 & \(\times\) & \(\times\) & 0.48 & 0.50 & 0.47 & 0.82 & 0.58 & 0.53 & 0.67 \\ \hline \multirow{2}{*}{[19]} & EMO-DB & 0.88 & 0.95 & \(\times\) & 0.84 & 0.95 & 0.84 & 0.95 & 0.95 & \(\times\) & 0.93 \\ \hline \multirow{2}{*}{[1]} & EMO-DB & 0.88 & 0.95 & \(\times\) & 0.84 & 0.95 & 0.84 & 0.95 & 0.95 & \(\times\) & 0.93 \\ \hline \multirow{2}{*}{[21]} & EMO-DB & 0.91 & 0.90 & \(\times\) & 0.87 & 0.92 & 0.66 & 0.85 & 0.88 & \(\times\) & 0.85 \\  & RAVDESS & 0.95 & \(\times\) & 0.95 & 0.86 & 0.91 & 0.43 & 0.50 & 0.61 & 0.95 & 0.77 \\ \hline \multirow{2}{*}{Proposed model} & EMO-DB & 0.96 & 0.91 & \(\times\) & 0.96 & 0.99 & 1.00 & 0.97 & 0.99 & \(\times\) & **0.97** \\  & SAVEE & 0.94 & \(\times\) & \(\times\) & 0.90 & 0.91 & 0.95 & 0.95 & 0.94 & 0.93 & **0.93** \\ \cline{1-1}  & RAVDESS & 0.95 & \(\times\) & 0.93 & 0.92 & 0.94 & 0.95 & 0.89 & 0.91 & 0.93 & **0.92** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of the SER method and the baseline methods utilizing the EMO-DB, SAVEE, and RAVDESS datasets.  
a novel method to address these issues through hybrid features (MFCCs and t-main) and 1D CNN to extract high-level patterns from audio data. The proposed method for each dataset recognized the emotions from audio signals with high accuracy. Secondly, this research resolves the problem of computation time by employing a lightweight and simple 1D CNN that used three CLs, two max-pool layers, three dropouts, and an FC layer with softmax classifier to identify emotions from audio files. To show the effectiveness, this research evaluated the efficiency of the proposed method with existing methods and the results showed that the proposed method outperformed the baseline methods by enhancing the number of accurately recognized instances. The potential reason for the enhanced recognition rate is that this study combined both the f-domain and t-domain representations of the audio signal to enhance the robustness, diversity, reliabilities, and to increase the generalization of the proposed SER method.