Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN
Understanding others correctly is of great importance for maintaining effective communication. Factors such as hearing difficulties or environmental noise can disrupt this process. Lip reading offers an effective solution to these challenges. With the growing success of deep learning architectures,...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| author | Ali Erbey Necaattin Barışçı |
| author_facet | Ali Erbey Necaattin Barışçı |
| author_sort | Ali Erbey |
| collection | DOAJ |
| description | Understanding others correctly is of great importance for maintaining effective communication. Factors such as hearing difficulties or environmental noise can disrupt this process. Lip reading offers an effective solution to these challenges. With the growing success of deep learning architectures, research on lip reading has gained momentum. The aim of this study is to create a lip reading dataset for Turkish digit recognition and to conduct predictive analyses. The dataset has divided into two subsets: the face region and the lip region. CNN, LSTM, and 3DCNN-based models, including C3D, I3D, and 3DCNN+BiLSTM, were used. While LSTM models are effective in processing temporal data, 3DCNN-based models, which can process both spatial and temporal information, achieved higher accuracy in this study. Experimental results showed that the dataset containing only the lip region performed better; accuracy rates for CNN, LSTM, C3D, and I3D on the lip region were 67.12%, 75.53%, 86.32%, and 93.24%, respectively. The 3DCNN-based models achieved higher accuracy due to their ability to process spatio-temporal data. Furthermore, an additional 1.23% improvement was achieved through ensemble learning, with the best result reaching 94.53% accuracy. Ensemble learning, by combining the strengths of different models, provided a meaningful improvement in overall performance. These results demonstrate that 3DCNN architectures and ensemble learning methods yield high success in addressing the problem of lip reading in the Turkish language. While our study focuses on Turkish digit recognition, the proposed methods have the potential to be successful in other languages or broader lip reading applications. |
| format | Article |
| id | doaj-art-fb500c6d5c344e7d8b87401151166d99 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-fb500c6d5c344e7d8b87401151166d992025-01-24T13:19:50ZengMDPI AGApplied Sciences2076-34172025-01-0115256310.3390/app15020563Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNNAli Erbey0Necaattin Barışçı1Department of Computer Programming, Distance Education Vocational School, Usak University, Usak 64200, TürkiyeDepartment of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06560, TürkiyeUnderstanding others correctly is of great importance for maintaining effective communication. Factors such as hearing difficulties or environmental noise can disrupt this process. Lip reading offers an effective solution to these challenges. With the growing success of deep learning architectures, research on lip reading has gained momentum. The aim of this study is to create a lip reading dataset for Turkish digit recognition and to conduct predictive analyses. The dataset has divided into two subsets: the face region and the lip region. CNN, LSTM, and 3DCNN-based models, including C3D, I3D, and 3DCNN+BiLSTM, were used. While LSTM models are effective in processing temporal data, 3DCNN-based models, which can process both spatial and temporal information, achieved higher accuracy in this study. Experimental results showed that the dataset containing only the lip region performed better; accuracy rates for CNN, LSTM, C3D, and I3D on the lip region were 67.12%, 75.53%, 86.32%, and 93.24%, respectively. The 3DCNN-based models achieved higher accuracy due to their ability to process spatio-temporal data. Furthermore, an additional 1.23% improvement was achieved through ensemble learning, with the best result reaching 94.53% accuracy. Ensemble learning, by combining the strengths of different models, provided a meaningful improvement in overall performance. These results demonstrate that 3DCNN architectures and ensemble learning methods yield high success in addressing the problem of lip reading in the Turkish language. While our study focuses on Turkish digit recognition, the proposed methods have the potential to be successful in other languages or broader lip reading applications.https://www.mdpi.com/2076-3417/15/2/563lip-readingensemble learning3DCNN |
| spellingShingle | Ali Erbey Necaattin Barışçı Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN Applied Sciences lip-reading ensemble learning 3DCNN |
| title | Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN |
| title_full | Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN |
| title_fullStr | Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN |
| title_full_unstemmed | Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN |
| title_short | Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN |
| title_sort | lip reading classification of turkish digits using ensemble learning architecture based on 3dcnn |
| topic | lip-reading ensemble learning 3DCNN |
| url | https://www.mdpi.com/2076-3417/15/2/563 |
| work_keys_str_mv | AT alierbey lipreadingclassificationofturkishdigitsusingensemblelearningarchitecturebasedon3dcnn AT necaattinbarıscı lipreadingclassificationofturkishdigitsusingensemblelearningarchitecturebasedon3dcnn |