# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 4 Results and Discussion
Figure 8 shows the accuracy values graphs of the Ensemble model during training for DataSet2. When examining the graphs in Figure 8, it is evident that the ensemble model's training and validation losses, as well as accuracy rates on DataSet2, change significantly over time. The training loss drops rapidly within the first 10 epochs and stabilizes, while the validation loss decreases more slowly and shows some fluctuations after the 20 th epoch, indicating an improvement in the model's generalization ability. The training accuracy \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline CNN & 55.47 & 60.13 & 57.48 & 63.12 \\ LSTM & 70.71 & 71.18 & 70.82 & 74.53 \\ C3D & 83.91 & 85.16 & 84.78 & 86.32 \\ I3D & 91.77 & 90.12 & 89.23 & 93.24 \\
3 DCNN + BiLSTM & 91.34 & 91.92 & 90.32 & 93.30 \\ C3D + I3D + (3 DCNN + BiLSTM) & 92.93 & 90.63 & 91.66 & 94.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance Metrics for Dataset2 Precision, Recall, F1-Score, Accuracy. Figure 8: Accuracy and loss plots for DataSet2. Figure 7: Ensemble learning scheme. steadily increases, while the validation accuracy rises more gradually and starts to converge after the 40 th epoch. These trends suggest that the model is learning effectively without signs of overfitting. Furthermore, through the use of majority voting in the ensemble learning process, a performance improvement of 2.17% was achieved for DataSet1 and 1.23% for DataSet2. The evaluation metrics provide crucial insights into the model's classification effectiveness. By analyzing these metrics, we can determine how well the model distinguishes between different classes. In Table 7, the precision, recall, and F1 score values are provided. The results presented in Table 7 evaluate the performance of the Ensemble Learning method. The metrics of Precision, Recall, and F1-Score indicate the accuracy of classifying Turkish numerals (0-9).The model demonstrates notable performance across most digits, with Precision values ranging from 0.8767 for digit 4 to 0.9291 for digit 7. Recall scores highlight the model's strong ability to correctly predict digits 0, 1, 7, 8, and 9, with values exceeding 0.94 for these digits. The F1-Scores, which provide a balanced measure of precision and recall, show the highest performance for digit 7 (0.9468), closely followed by digits 1 and 0, which yield F1-Scores of 0.9225 and 0.9222, respectively. The model encounters some challenges with digits 2, 3, 4, and 5, where the Recall scores are relatively lower (ranging from 0.8034 to 0.8333), leading to slightly lower F1-Scores, indicating room for improvement in detecting these specific digits. The lower recognition accuracy of the digits 2, 3, 4 and 5 can be attributed to the phonetic and linguistic features of the Turkish language. In Turkish, the number \"2\" (iki) is a two-syllable word, while the numbers \"3\" (ic), \"4\" (dort) and \"5\" (bes) are monosyllabic words. Monosyllabic words often contain more subtle and less pronounced lip movements, making them less visually distinct and difficult for lip-reading models to distinguish. The difficulty of discriminating words with few syllables is seen in English [49] as well as in Turkish. Also, the transition to the second syllable in the word \"two\" does not require a significant lip movement, which may reduce the model's ability to capture the distinctive features of this word. These observations underscore the impact of language-specific phonetic and grammatical features on the performance of lipreading models and highlight the need for further adaptation and refinement to address these nuances in Turkish. Overall, the ensemble model exhibits consistent high performance, particularly for digits 0, 1, 7, 8, and 9.