# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 6 Results and Discussion
### Spoken Language Identification Results  
Table 6 summarizes the results for Experiments 1 and 11, as listed in Table 4. Table 6 shows that the overall testing accuracy per file was better than per sequence, suggesting a level of similarity between the languages in the file sequences. The average per-file accuracy of five runs was 91.76% in Experiment 1 and 92.81% in Experiment 11. The highest accuracy per sequence was 83.73% in Experiment 1 and 84.08% in Experiment 11, while the worst accuracy was 82.18% in Experiment 1 and 83.58% in Experiment 11. On the other hand, the highest per-file accuracy was 92.21% in Experiment 1 and 93.11% in Experiment 11, while the lowest accuracy was 91.25% in Experiment 1 and 92.46% in Experiment 11. As shown in Table 6, the standard deviation is low, especially in Experiment 11, which means that the accuracies of all runs are too close to the average accuracy. In addition, the system is yielding more stability regarding classification robustness. The choice between accuracy or time is a well-known dilemma, and many studies have dealt with the discussion of the interpretability vs. performance trade-off. It is not always true that more complex models produce more accurate results. However, this can be incorrect when the given data is structured and has meaningful features. The statement \"models that are more complicated are more accurate\" can be valid in cases when the function being approximated is complex, the given data is widely distributed among suitable values for each variable, and the given data is adequate to build a complex model. The trade-off between interpretability and performance is evident in this circumstance [52, 53]. In our work, the Experiment 11 results were favorable compared to the Experiment 1 results regarding the proposed system's accuracy, while Experiment 1 outperformed Experiment 11 regarding the speed of execution. That means Experiment 1 is around 0.9% less accurate (\((93-92.21)/92.21)\) but 27% faster (\((221-162)/221\)) than Experiment 11. Figure 5 illustrates the language-identified accuracy results for the two experiments per sequence and file. The best language-identified accuracy was the Russian language, followed by Arabic. The worst language-identified accuracy was Spanish. In particular, two conventional architectures also revealed difficulties in discriminating between English and Spanish due to their similarity [11]. The Spanish language is also similar to the German language [54]. In contrast, the French language is similar to Spanish [21]. German and English are more likely to be confused, but English has a slightly stronger bias toward French [2]. All in all, the learned representations of the architecture are pretty distinctive for each language. For more details, Tables 7 and 8 present the confusion matrices of the average five runs per file for Experiments 1 and 11. In addition, the average accuracy, precision, recall, and F-measure results are presented. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Testing Accuracy** & \multicolumn{2}{c}{**Per Sequences (\%)**} & \multicolumn{2}{c}{**Per Files (\%)**} \\ \hline
**GTCC and MFCC** & **Experiment 1** & **Experiment 11** & **Experiment 1** & **Experiment 11** \\ \hline Run \# 1 & 83.73 & 83.77 & 92.21 & 93.00 \\ Run \# 2 & 82.31 & 83.61 & 91.79 & 92.61 \\ Run \# 3 & 82.18 & 83.93 & 91.25 & 92.86 \\ Run \# 4 & 82.28 & 84.08 & 91.86 & 93.11 \\ Run \# 5 & 82.44 & 83.58 & 91.68 & 92.46 \\
**Average** & 82.59 & **83.79** & 91.76 & **92.81** \\
**Standard Deviation** & 0.64 & **0.21** & 0.35 & **0.27** \\
**The Best Accuracy** & 83.73 & **84.08** & 92.21 & **93.11** \\ \hline
**The Worst Accuracy** & 82.18 & **83.58** & 91.25 & **92.46** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary results for selected experiments. were identified as German and six as Spanish. Moreover, twenty-one French test files were identified as German and five as Spanish.