# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 4 Results and Discussion
#### Word Dataset  
In order to evaluate the performance of the ensemble learning model on a different dataset, a new dataset composed of Turkish words was prepared, and experimental studies were conducted. During the creation of this dataset, existing datasets in the literature were  
Figure 9: Confusion matrix of the proposed ensemble model. reviewed, and the process of obtaining the dataset was initiated. When examining datasets such as GRID, OuluVs, and AvLetter, it was observed that the number of repetitions ranged between 3 and 5 on average. The number of repetitions in the datasets is shown in Table 8.  
In this context, the dataset was created with 4 repetitions for each sample. A total of 18 speakers participated in the process of creating the dataset, which consists of 150 words. By multiplying the number of speakers (18) with the number of repetitions (4) and the total number of words (150), a total of 10,800 videos were collected. During the selection of the words, a process was carried out to identify the most frequently used words on news websites. This process was designed for the purpose of word recognition, and the categories of news articles used in the dataset were used only as a reference to identify frequent words. This approach to word selection aims to contribute to making lip-reading systems more practical and useful, especially for hearing impaired individuals. In the process of identifying the most frequently occurring words in news websites, a dataset of 41,992 news articles in 13 categories published by the Kemik Natural Language Processing Group was utilized. Table 9 shows the categories in the dataset. The Natural Language Toolkit (NLTK), a natural language processing library, was used to determine the words to be included in the dataset. During the preprocessing stage, tokenization, removal of punctuation and numbers, normalization, and the elimination of stopwords were performed. Through tokenization, the text was split into words and stored as an array. As a result of this process, the total word count was calculated as 9,855,099. Subsequently, punctuation marks and numbers were removed, allowing for more accurate word matching. In the normalization step, the distinction between uppercase and lowercase letters was eliminated to ensure correct matching of the words. The removal of stopwords refers to the process of eliminating words in the text that do not affect its meaning. During preprocessing, stopwords were removed. NLTK is an open-source natural language processing library developed for the Python programming language. Some of the Turkish stopwords included in the NLTK library are as follows: bu (this), gibi (like), daha (more). After the words were determined, the video recording process began. A studio environment was set up for the video recordings using a Canon 6D Mark II camera. Once the environment was prepared, the recording process commenced, and data collection started. Figure 10 shows the video recording environment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Repetitions** \\ \hline AvLetters & 3 \\ AvLetters2 & 3–7 \\ OuluVS & 5 \\ OuluVS2 & 3 \\ M2 VTS [50] & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Number of repetitions of data sets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class Name** & **Number of Articles** & **Class Name** & **Number of Articles** \\ \hline World & 3724 & Health & 1383 \\ Economy & 3265 & Politics & 1849 \\ General & 6673 & Sports & 9997 \\ Current Affairs & 5847 & Technology & 771 \\ Culture \& Arts & 1155 & Turkey & 1939 \\ Entertainment & 2792 & Life & 644 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Distribution of News Articles Across Various Categories. The data was collected by having 18 individuals repeat 150 words 4 times. The dataset was recorded at a resolution of \(1920\times 1080\) and a frame rate of 50 frames per second (fps).