# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 4 Results and Discussion  
In this study, CNN, LSTM, C3D, I3D, 3 DCNN + BiLSTM models were used in the training phase of the data set. The performance of these models in the Turkish numeral dataset was compared. Experiments were performed on DataSet1 and DataSet2 separately. Experiments were performed on NVidia RTX 3060 graphics card using Pytorch deep learning library. While we can evaluate the time dimension for 3 DCNN models during the training phase, the frames are concatenated into a single frame in order to evaluate the time dimension using 2 DCNN models. Figure 5 shows the diagram in which the frames are given as input to the model. In this study, since lip reading involves both spatial (lip positions) and temporal (movement flow) information, 3 DCNN architectures (C3D, I3D) were preferred. 3 DCNN provides stronger spatio-temporal analysis by preserving connections between frames. This capability is crucial for accurately modeling lip movements.The input of the data and the schematization of the model in 3 DCNN models are shown in Figure 6.  
During the training phase of the models, we employed the grouped 5-fold cross-validation method. This approach entails dividing the dataset into five subsets, with training performed on four subsets and validation on the remaining one, rotating through all subsets to ensure a thorough evaluation. The data obtained in the study were collected from 78 people and subjected to training and testing using grouped 5-fold cross-validation. 5-fold cross-validation is a widely used technique in machine learning for evaluating model performance. This method typically involves dividing the dataset into five equal parts (folds), where each fold is successively used as the test set, while the remaining folds are used for training the model. After randomly splitting the dataset, one fold is used as the test set in each iteration, and the other four folds are used for training. This process is repeated five times, with each fold serving as the test set once. The results of each iteration are recorded, and the average performance score is calculated to evaluate the model's overall effectiveness. 5-fold cross-validation helps reduce the risk of overfitting by ensuring that each part of the dataset is used for both training and testing, allowing the model's consistency across different data subsets to be measured. However, this method can become computationally expensive for large datasets due to the need for retraining the model in each iteration. Although 5-fold cross-validation provides a balanced approach to evaluating a dataset, it may have limitations depending on the structure of the dataset. Specifically, in this study, the use of videos from different individuals may make random splitting less effective for proper evaluation. If the same individual's videos are present in both the training and test sets, the model may learn person-specific information (data leakage), leading to an unfairly inflated performance. To avoid this, the grouped 5-fold cross-validation method should be employed. In this approach, the videos of each individual are grouped within the same fold, ensuring that each iteration uses a different group of individuals for the training and test sets. This guarantees that videos from the same individual do not appear in both the training and test sets, allowing the model to be tested on completely unseen individuals,  
Figure 5: Frame input and model for CNN. Figure 6: Input for 3 DCNN models. thus providing a more accurate measure of generalization. Grouped 5-fold cross-validation is particularly beneficial for datasets like lip-reading, where person-specific data could skew the model's performance. Compared to standard 5-fold cross-validation, this method offers more reliable results. If grouped 5-fold cross-validation is not applied, there is a higher risk of over-fitting due to the potential overlap of individual-specific data across the sets, which may result in a misestimation of the model's generalization performance.