# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 5 Conclusions
In this context, it can be said that the extraction of the lip region is important in lip reading studies. When the results obtained as a result of the training of the model are examined, it is seen that the digits can be determined by the Turkish lip reading method. In particular, it is seen that the accuracy of some figures is higher than others. It is thought that the reason for the difference in accuracy rates in digits is that the number of syllables of the numbers with high accuracy is higher than the number of syllables with low accuracy. For some figures, it can be said that the lip area makes sharp movements that will enable more specific inferences to be made. While our study demonstrates promising results for Turkish digit recognition, the applicability of our approach to broader lip-reading tasks and other languages remains an open question. Future research should explore the scalability of our models by testing them on more diverse datasets, including continuous speech and other languages with different phonetic characteristics. While our study focuses on Turkish digit recognition, the methods and findings have potential applications in broader contexts. Future research could extend these techniques to continuous speech recognition and other languages with different phonetic and morphological structures. This would test the scalability and generalization capability of the models, providing further insights into their robustness across diverse linguistic datasets. Our findings suggest that 3 DCNN architectures combined with ensemble learning methods achieve higher accuracy in Turkish digit lip-reading. However, there is room for improvement in model performance, particularly for digits with lower recognition rates. Future work should focus on expanding the dataset, exploring other deep learning architectures, and applying our approach to continuous speech recognition tasks. Additionally, investigating the model's performance on other languages will provide valuable insights into its generalization and robustness. In this study, a new dataset containing not only numbers but also words has been developed. This extended dataset allows the model's performance to be tested on broader lip-reading tasks. The experiments conducted on both numbers and words demonstrate the model's adaptability to different linguistic structures. By enabling training and testing processes with diverse word groups, the new dataset ensures that the scope of the study goes beyond digit recognition. Future studies can expand the scope by applying these architectures to other languages or testing them on more complex sentence structures. In this study, when the examples that the model mislabeled after the learning process were examined, it was seen that the errors were caused by the sample. For further studies, the success rate can be increased by increasing the number of frames used in each sample during data augmentation and improving factors such as light and resolution during data collection. A Turkish lip reading system can be created by working with parameters such as different filter sizes and number of layers on different data. One of the most significant contributions of this study to the literature is the introduction of a unique lip-reading dataset for Turkish. Furthermore, enhancing performance by combining 3 DCNN and 13D models with BiLSTM using the ensemble learning method is a novel approach introduced for the first time in the literature. This study offers a methodology that not only contributes to Turkish lip-reading research but also serves as a reference for future lip-reading research in other languages. Future research should aim to expand the dataset to include more diverse linguistic contexts and continuous speech recognition. Additionally, exploring other deep learning architectures and techniques, such as transformer models, may offer further improvements.