# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 3 Material and Method  
### Dataset  
In this study, we developed a dataset specifically for Turkish digit recognition through lip reading. The dataset consists of videos where participants spoke digits from 0 to 9, with each digit repeated five times. This structure results in a dataset containing 10 distinct classes. Additionally, the dataset includes an extended subset featuring words beyond digits, enabling broader experimentation. The data was collected from 78 participants, representing a diverse set of individuals. The data collection procedures were carried out with the approval of the Usak University Social and Humanities Scientific Research and Publication Ethics Committee with the approval number 2024-208. Signed informed consent was obtained from all participants during the data collection phase. Participants ranged in age from 20 to 55 years, and each participant repeated each digit five times, yielding a total of 3900 video clips. The videos were recorded using an iPhone at a resolution of 1080 \(\times\) 1920 and a frame rate of 30 frames per second (fps). The videos were saved in MOV format. Preprocessing steps included frame extraction and isolating the lip regions using the RetinaFace [28] method. In this study, rotation was applied as a data augmentation technique to increase the diversity and robustness of the dataset. The video frames were rotated 10 degrees to the left and 10 degrees to the right to increase the robustness of the dataset by preserving the natural and unique features of the lip movements. Data augmentation was performed before splitting the dataset. However, since the dataset was grouped according to participants, the order of data splitting has no significance for the risk of data leakage. This preserves the integrity and reliability of the experimental process and supports the validity of the results. Figure 1 presents demographic details, including the age and gender distribution of the participants. The data set consists of 57 male and 21 female participants. All participants are of Turkish origin, and the dataset comprises a total of 3900 video files, representing the 78 participants each reciting 10 digits five times. As can be seen in Figure 1, while selecting the sample, on the one hand, generalization was considered, but in order for the system to achieve high performance of the model, we aimed to distribute the age limit between 20 and 55. In the study of [PERSON] et al. it was stated that lip movements weaken with advancing age [29]. For this reason, the data were obtained in a right-skewed distribution. This situation creates a difference in age distribution in sample selection. Similarly, [PERSON] et al. examined the effect of variability in lip movements on motor control skills and found a negative correlation between age and these skills, which is in parallel with our sample selection [30]. Figure 2 illustrates sample frames from the videos, demonstrating of participants' lip movements during the pronunciation of the digits. The videos were recorded from the front angle, ensuring a consistent view of the participants' faces. The dataset was further processed by cutting 420 pixels from the top and bottom of each frame to focus specifically on the facial and lip regions. The final frame resolution for each video was standardized at 1080 \(\times\) 1080 pixels. Table 2 provides detailed specifications of the dataset, including participant demographics, video resolution, and frame rate. This dataset provides a valuable resource for the development of deep learning models focused on lip reading for Turkish, filling a gap in the current literature that is heavily