Home⇛Isabela State University Linker: Journal of Engineering, Computing and Technology⇛vol. 2 no. 1 (2025)

Transcribing Filipino Syllables into Baybayin Script Using Convolutional Neural Network with Long Short-Term Memory Architecture for Spoken Tagalog Recognition

Steven N. Epis | Chelsea Mariae T. Panugaling | Abegail C. Jamel | Daisy B. Decio | Jose C. Agoylo Jr

Discipline: Artificial Intelligence

 

Abstract:

This study describes the use of machine learning technologies in the conversion of spoken Tagalog from syllables to the Baybayin script, which was used in the Philippines long before the coming of the Spaniards. The model integrated audio data that dealt with phonetic aspects and correct mapping to Baybayin symbols. The model's overall accuracy is 96%, which in turn shows the reliability of performance in segment speaking of Tagalog into Baybayin text. The CNN-LSTM architecture was proved effective, underscoring the potential of advanced speech recognition technologies for cultural preservation. By modeling the phonetic-to-symbolic relationships in spoken Tagalog, the system offers valuable contributions to linguistic research, especially in areas such as phonology, orthography, and morpho-syllabic analysis of Filipino, thereby bridging traditional scripts and modern language technologies. This study emphasizes the need for further dataset expansion and support for diverse linguistic variations to enhance the system’s inclusivity and applicability. It is an important addition to technology-based cultural preservation, paving the way for similar projects in other languages and scripts. Further studies may enhance this system and make a transcription into sentences, phrases, and paragraphs.



References:

  1. Amoguis, A. I. V., del Rosario, L. T. C., & Bañares, G. J. A. (2023). Baybayin character instance detection. arXiv. https://arxiv.org/abs/2304.09469
  2. Bernardo, J. S. D., & Estuar, M. R. J. E. (2025). bAI-bAI: A context-aware transliteration system for Baybayin scripts. In Proceedings of the SEALP Workshop 2025 (pp. …). https://aclanthology.org/2025.sealp-1.1/
  3. Cabuay, C. (n.d.). Baybayin: The ancient script of the Philippines.
  4. Callos, V., Lozano, M. A., del Rosario, M. E., Tan, R. D., & Villapando, K. (n.d.). BAYBAYIN: Reviving the lost Filipino script using machine learning models. Asian Institute of Management.
  5. Cayme, K. J., Dela Cruz, J. M., Peña, M. J., & Vasquez, J. M. (2024). Gesture recognition of Filipino sign language using convolutional and long short-term memory deep neural networks. Knowledge, 4(3), 358–381. https://doi.org/10.3390/knowledge4030020
  6. Chan, A. J. L., de Leon, E. M. M., Lumibao, A. B. V., & Tolentino, D. J. B. (n.d.). Speech-to-text converter for Filipino language using hybrid artificial neural network / hidden Markov model. De La Salle University Repository. https://animorepository.dlsu.edu.ph/etd_bachelors/6016
  7. Franco, A. Y. (2021). Optical recognition of the Philippines’ ancient text: A deep learning approach. International Journal of Multidisciplinary Research and Analysis, 8(3). https://doi.org/10.47191/ijmra/v8-i03-40
  8. Goyal, P., Saffari, A., & Ebrahimi, T. (2019). Analyzing training curves for deep learning models: Convergence and overfitting trends. Neural Networks, 110, 148–160. https://doi.org/10.1016/j.neunet.2018.11.008
  9. Graves, A., Mohamed, A., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). https://doi.org/10.1109/ICASSP.2013.6638947
  10. Hernandez, A. A., Peralta, R. G., & Santos, M. A. (2020). Convolutional neural network for automatic speech recognition of Filipino language. International Journal of Advanced Trends in Computer Science and Engineering, 9(1). https://doi.org/10.30534/ijatcse/2020/0791.12020
  11. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Kingsbury, B., & Sainath, T. N. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 29(6), 82–97. https://doi.org/10.1109/MSP.2012.2205597
  12. Hsu, J.-Y., Chen, Y.-J., & Lee, H. (2020). Meta Learning for End-To-End Low-Resource Speech Recognition. International Conference on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/icassp40776.2020.9053112
  13. Lim, A., & Manipon, C. (n.d.). Preserving indigenous scripts: The Baybayin example.
  14. Oraño, J. F. V., Malangsa, R. D., & Tangcawan, C. G. (2022). Using deep learning and adaptive thresholding approach for image-based Baybayin to Tagalog word transliteration. In Proceedings of the IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). https://doi.org/10.1109/HNICEM57413.2022.10109553
  15. Patungan, A. (2023). A machine learning modeling prediction of enrollment among admitted college applicants at University of Santo Tomas. AIP Conference Proceedings, 3001(1), 020005. https://doi.org/10.1063/5.0132391
  16. Pino, R., Santos, L., Dela Peña, C., & Robles, G. (2011). Optical character recognition system for Baybayin scripts using support vector machine. Journal of Computational Linguistics, 12(3), 45–58.
  17. Recario, R., Mangahas, E., Villareal, T., & Cruz, M. (2011). OCR-based systems: Evaluating accuracy in Filipino text recognition. Journal of Computational Linguistics, 12(3), 45–58.
  18. Slam, W., Li, Y., & Urouvas, N. (2023). Frontier research on low-resource speech recognition technology. Sensors, 23(22). https://doi.org/10.3390/s23229096
  19. Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence-to-sequence learning with neural networks. In Advances in Neural Information Processing Systems (NeurIPS). https://doi.org/10.48550/arXiv.1409.3215
  20. Zhang, Y., Chan, W., & Jaitly, N. (2017). Very deep convolutional networks for end-to-end speech recognition. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4845–4849). https://doi.org/10.1109/ICASSP.2017.7953077

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