# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 5 Results and Discussion  
### Experimental Results and Evaluation  
The measurement of classifiers in terms of quality involves the use of accuracy, precision, recall, and F1-measure. One of the most crucial performance measures is accuracy. It is defined as the ratio of observation predicted accurately to all of the observations predicted. It is usually believed that the model with the highest accuracy is best. While the significance of accuracy as an important measure cannot be denied, measuring performance also calls for the availability of symmetric datasets having close or similar values for false positives and false negatives. Hence, a model's performance evaluation must involve additional parameters. The ratio of the predicted positive observations that are accurate to the overall predicted positive observations is referred to as the precision. High precision is associated with a low false positive rate. Recall (sensitivity)--Recall refers to the ratio of predicted positive observations that are accurate to the overall observations with respect to actual class-yes. Any value exceeding 0.5 is considered as appropriate. F1 score--Computation of the weighted average of precision and recall yields the F1 score. This implies the inclusion of false positives as well as false negatives in the F1 score. This concept involves more complexity as compared to accuracy; however, F1 outshines accuracy when it comes to usefulness, particularly in the case of irregular class distribution. The higher the similarity of costs of false positives and false negatives, the higher the accuracy will be. We must consider precision as well as recall in the case of high diversity in the cost of false positives and false negatives. This research employed a novel RBFNN-CNN that was evaluated against multiple machine learning algorithms using the Emirati speech database and global English SUSAS database in stressful talking environments. The stressful talking environments included \"neutral, shouted, slow, loud, soft, and fast-talking conditions\". Tables 2 and 3 demonstrate average speaker identification performance in stressful talking conditions using the Emirati and SUSAS datasets, respectively. In Table 2, the standard deviation is added as one of the measurements. A large standard deviation indicates that the data are dispersed, which is unreliable. However, a low standard deviation indicates that the data are tightly grouped around the mean, which is more reliable. The conducted results show that the standard deviation outcomes were relatively small; thus, the data were reliable. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best Test** & **Standard** & **Precision** & **Recall** & **F1** \\  & **Accuracy** & **Deviation** & **Weighted** & **Weighted** & **Weighted** \\ \hline SVM & 87\% & 3.01 & 97\% & 87\% & 0.87 \\ MLP & 69\% & 3.51 & 74\% & 69\% & 0.69 \\ KNN & 62\% & 3.60 & 63\% & 63\% & 0.62 \\ CNN\_BLSTM & 88\% & 2.90 & 89\% & 88\% & 0.87 \\ Att-BILSTM & 79\% & 3.24 & 82\% & 79\% & 0.77 \\ BI-LSTM & 78\% & 3.36 & 81\% & 78\% & 0.78 \\ BIGRU & 73\% & 3.44 & 76\% & 73\% & 0.73 \\ GRU & 70\% & 3.49 & 72\% & 70\% & 0.69 \\ LSTM & 62\% & 3.57 & 64\% & 62\% & 0.62 \\
**Proposed** & **98\%** & **1.80** & **98\%** & **98\%** & **0.98** \\
**RBFNN-CNN** & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average speaker identification performance using Emirati accent dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Best Test** & **Precision** & **Recall** & **F1** \\  & **Accuracy** & **Weighted** & **Weighted** & **Weighted** \\ \hline SVM & 90\% & 90\% & 90\% & 0.90 \\ MLP & 91\% & 92\% & 92\% & 0.92 \\ KNN & 70\% & 71\% & 70\% & 0.70 \\ CNN\_BILSTM & 92\% & 93\% & 92\% & 0.92 \\ Att-BILSTM & 84\% & 84\% & 84\% & 0.84 \\ BI-LSTM & 80\% & 81\% & 80\% & 0.81 \\ BIGRU & 77\% & 78\% & 77\% & 0.77 \\ GRU & 73\% & 74\% & 73\% & 0.73 \\ LSTM & 63\% & 65\% & 63\% & 0.63 \\
**Proposed** & **98\%** & **98\%** & **98\%** & **0.98** \\
**RBFNN-CNN** & & & & \\ \hline \hline \end{tabular}
\end{table}