# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 5 Result and Discussion  
The result obtained in this work proved the efficiency of our proposed model. In the first stage of our experiment, we carried out training of the additional (topmost) layers of the feature extraction segment of our proposed model, which comprises a dropout layer, two dense layers, and an activation function, since all layers of our based transfer learning network are non-trainable. We obtained a significant accuracy and loss curve (94%, 14%) after training and validation was carried out with the dataset as indicated in Figure 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Optimizer & Adam \\ Loss Function & Sparse Categorical Cross-Entropy \\ Activation Function & Softmax \\ Environment & CPU \\ Output Classes & 7 \\ Learning Rate & 0.001 \\ Maximum Epochs & 50 \\ MLP Hidden Neurons & 20 \\ SVM Kernel Function & Linear \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Implementation Parameters. A normalized confusion matrix which depicts a holistic insight of various results obtained from our experiments with different classifiers is shown in Figures 3-5, respectively. Looking at the confusion matrix, for the TESS dataset with the MLP classifier, 100% accuracy was recorded for angry, disgust, fear and happy emotion, while the surprise emotion yielded the lowest accuracy of 89%, and others were above 90%. With the SVM classifier, the minimum accuracy increased to 90%. On the EMO-DB dataset, our model achieved 94%, 89%, 98%, 94%, 91%, 98% and 99% accuracy in classification for angry, boredom, disgust, fear, happy, neutral and sad emotion using the MLP classifier. When SVM was introduced, there was a slight improvement in recognition accuracy, as the three emotions (disgust, neutral and sad) recorded 99% accuracy while the happy emotion recorded the lowest accuracy of 87%. As with the combined dataset of TESS-EMO-DB, the highest accuracy of 99% was recorded for the sad, neutral and disgust emotions with the MLP classifier, while the happy emotion recorded the lowest accuracy of 87%, as depicted in Figure 6. For the SVM classifier, only the sad emotion produced the highest accuracy of 99%, while others were 90% and above. As we analyze further, the classification report comprising accuracy, F1-score, specificity and sensitivity evaluation metrics was employed in order to fully grasp how efficient our proposed method performed in its discriminatory ability to learn features for speech emotion classification. As shown in Table 4, we achieved the highest accuracy of 97.2% on the TESS dataset with the SVM classifier, while the lowest accuracy of 94.3% was recorded  
Figure 4: Confusion matrix of emotion classification on TESS dataset. (a) DCNN-NCA-MLP. (b) CNN-NCA-SVM. Figure 3: Proposed model’s accuracy and loss performance curves for both training and validation. from the EMO-DB dataset with the MLP classifier. The highest specificity and sensitivity of 100% was recorded with experiments on the two datasets for both classifiers, while 98.8% specificity was recorded on the combined dataset. As with F1-score, we achieved a state-of-the-art result of 94.4%, 95.7% and 97%, respectively after evaluation on the three datasets. A return of operating curve (ROC) was adopted to present the graphical outlook of classification results of six emotions from the hybrid dataset. The ROC analysis result for six emotions on the combined dataset is illustrated in Figure 7. On the curve, each class represents an emotion: angry, sad, disgust, happy, fear and neutral with class 0 to 5, respectively. From the curve, the highest classification performance of Average Area Under the Curve (AUC) of 98% was achieved on classes 3 and 5 (neutral and disgust), while class 4 (fear) yielded the lowest AUC of 50%.