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LIP READING USING CNN FOR TURKISH NUMBERS
Öz
Recently, lip reading has become one of the most important fields of study in the field of artificial intelligence. In this study, lip reading process was performed in Turkish language using convolutional neural networks (CNNs). For this purpose, people were asked to record the numbers video (61 video), and 9 video also collected from YouTube. The dataset was collected for 20 numbers. In this study, only the video was used and the sounds were completely removed. Due to the small dataset, it was tried to reproduce with different methods. The model was trained on the train dataset and 56.25% success was achieved on the test dataset.
Anahtar Kelimeler
Kaynakça
- Agrawal, S., & Omprakash, V. R. (2016, July). Lip reading techniques: A survey. In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (pp. 753-757). IEEE.
- Chen, X., Du, J., & Zhang, H. (2020). Lipreading with DenseNet and resBi-LSTM. Signal, Image and Video Processing, 14(5), 981-989.
- Chung, J. S., Senior, A., Vinyals, O., & Zisserman, A. (2017, July). Lip reading sentences in the wild. In 2017 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3444-3453). IEEE.
- Elrefaei, L. A., Alhassan, T. Q., & Omar, S. S. (2019). An Arabic visual dataset for visual speech recognition. Procedia Computer Science, 163, 400-409.
- Faisal, M., & Manzoor, S. (2018). Deep learning for lip reading using audio-visual information for urdu language. arXiv preprint arXiv:1802.05521.
- Garg, A., Noyola, J., & Bagadia, S. (2016). Lip reading using CNN and LSTM. Technical report, Stanford University, CS231 n project report.
- Li, Y., Takashima, Y., Takiguchi, T., & Ariki, Y. (2016, June). Lip reading using a dynamic feature of lip images and convolutional neural networks. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (pp. 1-6). IEEE.
- Martinez, B., Ma, P., Petridis, S., & Pantic, M. (2020, May). Lipreading using temporal convolutional networks. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6319-6323). IEEE.
Ayrıntılar
Birincil Dil
İngilizce
Konular
-
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
31 Aralık 2022
Gönderilme Tarihi
12 Nisan 2022
Kabul Tarihi
2 Eylül 2022
Yayımlandığı Sayı
Yıl 2022 Cilt: 5 Sayı: 2
APA
Pourmousa, H., & Özen, Ü. (2022). LIP READING USING CNN FOR TURKISH NUMBERS. Journal of Business in The Digital Age, 5(2), 155-160. https://doi.org/10.46238/jobda.1100903
AMA
1.Pourmousa H, Özen Ü. LIP READING USING CNN FOR TURKISH NUMBERS. JOBDA. 2022;5(2):155-160. doi:10.46238/jobda.1100903
Chicago
Pourmousa, Hadı, ve Üstün Özen. 2022. “LIP READING USING CNN FOR TURKISH NUMBERS”. Journal of Business in The Digital Age 5 (2): 155-60. https://doi.org/10.46238/jobda.1100903.
EndNote
Pourmousa H, Özen Ü (01 Aralık 2022) LIP READING USING CNN FOR TURKISH NUMBERS. Journal of Business in The Digital Age 5 2 155–160.
IEEE
[1]H. Pourmousa ve Ü. Özen, “LIP READING USING CNN FOR TURKISH NUMBERS”, JOBDA, c. 5, sy 2, ss. 155–160, Ara. 2022, doi: 10.46238/jobda.1100903.
ISNAD
Pourmousa, Hadı - Özen, Üstün. “LIP READING USING CNN FOR TURKISH NUMBERS”. Journal of Business in The Digital Age 5/2 (01 Aralık 2022): 155-160. https://doi.org/10.46238/jobda.1100903.
JAMA
1.Pourmousa H, Özen Ü. LIP READING USING CNN FOR TURKISH NUMBERS. JOBDA. 2022;5:155–160.
MLA
Pourmousa, Hadı, ve Üstün Özen. “LIP READING USING CNN FOR TURKISH NUMBERS”. Journal of Business in The Digital Age, c. 5, sy 2, Aralık 2022, ss. 155-60, doi:10.46238/jobda.1100903.
Vancouver
1.Hadı Pourmousa, Üstün Özen. LIP READING USING CNN FOR TURKISH NUMBERS. JOBDA. 01 Aralık 2022;5(2):155-60. doi:10.46238/jobda.1100903
Cited By
Challenges and enhancements in Turkish automatic lip reading using deep learning models
Signal, Image and Video Processing
https://doi.org/10.1007/s11760-026-05252-2
