# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 4 Results and Discussion
Consequently, constructing validation and test sets with data from individuals not previously seen by the model is crucial for an accurate evaluation of model performance. The impact of hyperparameter values, including the number of epochs, batch size, learning rate, and chosen optimizer, on model performance is well-established during training. The hyperparameters employed in the training of the models, as determined through experimental studies, are presented in Table 4.  
The performance of each model was evaluated across DataSet1 and DataSet2, as summarized in Table 5. Notably, the 3D convolution-based models (C3D, I3D, 3 DCNN + BiLSTM) consistently outperformed their 2D counterparts (CNN, LSTM), underscoring the importance of preserving temporal information in video data processing. When the data in Table 5 are examined, it is seen that the results for DataSet2 obtained from the lip region are high. Resnet152 weights were used as pre-trained in the training phase of DataSet1 obtained from the face region with CNN. Batch size 4 was selected with a training period of 50 epochs. As a result of the training, 52.37% success was achieved. The training was then carried out within the LSTM and 66.51% success was found. Afterwards, DataSet1 and DataSet2 were trained with C3D and I3D models. Although LSTM architectures are effective in capturing dependencies in time-series data, they lack the capability to jointly process spatial and temporal features in video data. The effects of hyper parameter values such as epoch number, batch size, learning rate and selected optimizer on the performance of the model are known during the training of the model. In experimental studies, a training of 50 epochs was carried out for the training of the C3D model. In the training, a batch size of 4 and a learning rate of 0.01 were used. Stochastic Gradient Descent(SGD) was chosen as the optimizer. As a result of this training, an accuracy rate of 78.82% was found for DataSet1. As a result of training the C3D model with DataSet2, 86.32% accuracy rates were found. A training of 50 epochs was carried out for the training of the I3D model. In the training, a batch size of 4 and a learning rate of 0.001 were used. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model Name** & **Epoch** & **Optimizer** & **LR** & **Batch S.** & **Act. Func.** \\ \hline CNN & 50 & [PERSON] & 0.001 & 4 & ReLU \\ LSTM & 50 & Adam & 0.001 & 4 & Tanh \\ C3D & 50 & SGD & 0.01 & 4 & ReLU \\ I3D & 50 & Adam & 0.001 & 4 & ReLU \\
3 DCNN + BiLSTM & 50 & [PERSON] & 0.001 & 4 & ReLU \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model Architecture and Parameters. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Dataset1 Accuracy (\%)** & **Dataset2 Accuracy (\%)** \\ \hline CNN & 52.37 & 67.12 \\ LSTM & 66.51 & 74.53 \\ C3D & 78.82 & 86.32 \\ I3D & 87.17 & 93.24 \\
3 DCNN + BiLSTM & 87.72 & 93.30 \\ C3D + I3D+ (3 DCNN + BiLSTM) & 89.92 & 94.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance Metrics for Dataset1 and Dataset2. [PERSON] was chosen as the optimizer. As a result of this training, accuracy rates of 87.17% for DataSet1 and 93.24% for DataSet2 were found. Upon analyzing the obtained results, it is observed that the 3 DCNN + BiLSTM model achieved the highest performance among the I3D, C3D, and 3 DCNN + BiLSTM models. The 3 DCNN + BiLSTM model reached an accuracy rate of 87.72% for Dataset1 and 93.30% for Dataset2. The success rates of the C3D, I3D, and 3 DCNN + BiLSTM models can be attributed to their superiority in spatio-temporal inference. When the results obtained from both datasets are examined, it is evident that lip reading studies should focus on analyzing the lip region in the dataset. When the results of comparisons of all models are examined in Table 5, it is seen that higher accuracy rates can be achieved with the use of models that perform 3D convolution for time-dependent studies such as lip reading.