## 6 Experiments Results and Discussion
### Perceptual Test Results  
We performed an actual human perceptual test to check and validate system performance and to analyze its errors regarding the most mistaken test token. The human perceptual testers were six normal adult individuals. They were four men and two women.  
The selected dataset speakers with the highest number of errors were speaker 14M, with a 20.71% average rate of errors, speaker 24M, with an average of 23.57%, and speaker 27M, with 21.43%. Speaker 45M had an average error rate of 22.14%, speaker 49M had an average error rate of 22.86%, speaker 62M had an average error rate of 20.71%, and speaker 134F had an average error rate of 22.14%. The audio files chosen depend on the statistical analysis of the speakers that were missed by the system based on the graph.  
The perceptual test results are depicted in Table 9. In explanation, we could refer to the listener by their number and gender as 1M, which means speaker index 1, male gender, in the perceptual test participants. The triggering goal of this research was to design automatic speaker recognition. However, a human perceptual test was considered to verify and uncover if our system is correct after providing high errors for specific speakers.  
After analyzing these data and comparing them with system output, it was found that most listeners failed to predict more than 10 samples out of the 35 (i.e., with an error of more than 28.5%). These perceptual test results coincide with the designed automatic system in this work. Therefore, we can somehow conclude that the system we created with the suggested features closely resembles the human auditory and natural ability system, and any errors made by the system are likely due to issues with the used data set speakers and/or recordings. In other words, the main goal of considering a human perceptual test for the fatally recognized parts of our test subset is to have some cues about the correctness of the system's abnormal errors for specific speakers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Run Number** & **Accuracy** & **Recall** & **Precision** & **F1 Score** \\ \hline
**1** & 92.26\% & 93.03\% & 92.26\% & 92.64\% \\ \hline
**2** & 91.28\% & 92.16\% & 91.28\% & 91.72\% \\ \hline
**3** & **92.70\%** & **93.24\%** & **92.70\%** & **92.97\%** \\ \hline
**4** & 92.24\% & 92.67\% & 92.24\% & 92.45\% \\ \hline
**5** & 91.15\% & 91.90\% & 91.15\% & 91.53\% \\ \hline
**Average** & 91.93\% & 92.60\% & 91.93\% & 92.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The five BLSTM experiments’ results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Listener** & & & & & & & \\
**Number and** & **Speaker 14M** & **Speaker 24M** & **Speaker 27M** & **Speaker 134F** & **Speaker 45M** & **Speaker 49M** & **Speaker 62M** & **Total Miss** \\
**Gender** & & & & & & & & **62M** & **Pediction of the Test Samples** \\ \hline
**1M** & 3 of 5 & 1 of 5 & 4 of 5 & 3 of 6 & 3 of 5 & 1 of 5 & 0 of 4 & 15 of 35 \\ \hline
**2M** & 2 of 5 & 2 of 5 & 3 of 5 & 2 of 6 & 3 of 5 & 2 of 5 & 1 of 4 & 15 of 35 \\ \hline
**3M** & 0 of 5 & 3 of 5 & 1 of 5 & 3 of 6 & 0 of 5 & 0 of 5 & 2 of 4 & 9 of 35 \\ \hline