# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 5 Conclusions  
In this study, a data set was created to use in lip reading Turkish digit recognition with deep learning architectures. The dataset is divided into two separate subsets in which \begin{table}
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**Phase** & **Video Count** \\ \hline Original Dataset & 10,800 \\ After Data Augmentation (Training) & 34,713 \\ \hline \end{tabular}
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Table 10: Number of videos in original and augmented for word dataset. \begin{table}
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**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline CNN & 63.15 & 55.09 & 58.88 & 65.35 \\ LSTM & 65.45 & 61.09 & 63.20 & 66.42 \\ C3D & 65.81 & 72.55 & 69.03 & 66.78 \\ I3D & 70.77 & 67.92 & 69.31 & 68.13 \\
3 DCNN + BiLSTM & 75.12 & 73.21 & 74.15 & 77.69 \\ C3D + I3D + (3 DCNN + BiLSTM) & 77.91 & 76.43 & 77.16 & 78.63 \\ \hline \end{tabular}
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Table 11: Performance metrics of different models for word data set. both the facial region and the lip region are extracted. The created data set was trained with incorporating both 2 DCNN and 3 DCNN architectures after data preprocessing. CNN, LSTM and 3 DCNN based C3D, I3D, 3 DCNN + BiLSTM models were used in the trainings. Optimal results were experimentally determined by tuning different hyperparameters. In the 2 DCNN models, a single frame is generated by combining picture frames, prioritizing spatio-temporal information. As a result of the training, CNN and LSTM models achieved lower accuracy in both the data set containing the face region and the data set containing the lip region compared to C3D and I3D containing 3 DCNN. The C3D and I3D models were preferred for their superior ability to extract spatial information. In our study, the I3D model achieved a high success rate with an accuracy of 93.24%. In a study by [PERSON] and [PERSON] [51] using I3D on English words, they found 84.07% success. [PERSON] and [PERSON] used two-stream I3D and BiLSTM on the LRW dataset in their study and the study provided 5.3% improvement compared to previous state-arts. In this context, it is seen that I3D improves performance in lip reading studies. Similarly, ref. [52] used the C3D model in the pre-training stage in a multilayer approach, considering that it achieved higher performance than single layer in temporal tasks in their lip reading studies on LRW dataset. In this context, it can be said that models based on the 3 DCNN architecture produce more accurate results in lip reading studies. As a result of experimental studies, it is seen that 3 DCNN models are more successful. In our study, the 3 DCNN + BiLSTM model achieved the highest success rate with an accuracy of 93.30%. The ability of 3 DCNN models to process spatio-temporal data is seen as the reason for this success. It is seen that 3 DCNN models should be focused on for time-dependent video data and lip reading studies. It is seen that high performance can be achieved as a result of the correct selection of hyperparameters with good optimization techniques. When we extended our study with the ensemble learning technique, we used the 3 DCNN + BiLSTM model in addition to the C3D and I3D models that we achieved the highest successes. By using the majority voting method, which is one of the ensemble learning methods with the predictions of all models, we improved our best result by 1.23% to 94.53% and achieved a new state-of-the-art on our Dataset2 subset. In addition to accuracy, we evaluated model performance using precision, recall, and F1 score. Precision measures the proportion of true positive predictions among all positive predictions, recall measures the proportion of true positives among all actual positives, and F1 score provides a harmonic mean of precision and recall. These metrics offer a more nuanced evaluation of the models' performance. When the experimental results obtained are examined, when DataSet2 containing the lip region and DataSet1 containing the face region are compared, it is seen that the lip region achieves higher success in all experiments.