# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 4 Results and Discussion
The results obtained in both datasets may indicate the superiority of the 3 DCNN + BiLSTM model in integrating spatial and temporal features. For models with 2 DCNN, even concatenating frames to avoid losing time-dependent context does not achieve the success of 3 DCNN models. Moreover, the significant improvement in accuracy from CNN to LSTM models underscores the importance of considering temporal dynamics in video analysis tasks. However, the transition from LSTM to C3D and I3D models suggests that incorporating these dynamics through 3D convolutions directly within the network architecture is even more beneficial. This is particularly evident in the substantial leap in performance between C3D and I3D models, implying that I3D's approach to simultaneously modeling motion and appearance features is highly effective for complex, time-sensitive tasks. After reviewing the results of individual models, it becomes evident that 3D convolution-based models (C3D, I3D, 3 DCNN + BiLSTM) demonstrate clear superiority in integrating temporal information with spatial features. In particular, the I3D and 3 DCNN + BiLSTM models achieve higher accuracy in tasks requiring temporal dependencies, such as lip reading. This performance advantage stems from their ability to simultaneously model both motion and spatial characteristics. However, each model exhibits varying levels of success across different classes, highlighting that while some models perform exceptionally well for certain classes, they may fall short for others. To leverage the strengths of each model, ensemble learning techniques were employed, aiming to combine the diverse classification capabilities of these models. By incorporating ensemble methods, the goal is to enhance overall performance through the aggregation of predictions from models that individually excel in different aspects of the task. We extend our experimental work with ensemble learning. In this context, we use the 3 DCNN + BiLSTM model in addition to C3D and I3D, the two models with the highest rates. We vote on the prediction results from the models using majority learning. In ensemble learning studies, different methods such as averaging and majority voting can be adopted at this stage. At the same time, the weights assigned to the models can be used to change their impact value in voting. In this phase of the study, the weights of the models are taken as equal in the voting method we applied. Figure 7 shows the schematic of the ensemble learning method used. In this study, majority voting was chosen as the ensemble method due to its simplicity and effectiveness in combining the strengths of different models. Majority voting is particularly advantageous when individual models perform well in different areas, as it allows for a more balanced decision-making process by giving equal weight to each model's prediction. This method helps mitigate the risk of overfitting to any particular model's biases, as it relies on the consensus of multiple models rather than the dominance of a single one. Additionally, compared to more complex ensemble methods, such as weighted voting or stacking, majority voting is computationally less intensive and easier to implement. Since the performance of the individual models in this study (C3D, I3D, 3 DCNN + BiLSTM) is relatively close, giving each model equal importance ensures that the ensemble benefits from their combined strengths without the need for fine-tuning weight parameters, making it an efficient and robust choice for this specific application. The performance of the models was assessed using key metrics such as accuracy, precision, recall, and F1 score, allowing for a comprehensive and detailed evaluation of the models' effectiveness.The results of the evaluations obtained are given in Table 6.  
It is known that different techniques are used with ensemble learning. The ensemble learning technique used in this study is majority voting. After the experimental runs, 2.20% improvement was achieved in DataSet1 and 1.23% improvement was achieved in DataSet2.