Wearable Cleft Palate Speech Interpreter Using Deep Learning and Neural Networks Algorithm - Jaypee P. Canlas; Jose Paulo E. Mendoza; Josh Nathaniel B. Perez; Reynaldo L. De Asis; Asil Kastle S. Dela Cruz (International Journal of Progressive Research in Science and Engineering, 2024)
In a world where communication is important, individuals with cleft palates face difficult challenges in expressing themselves effectively. Traditional communication methods often fall short, hindering their ability to interact confidently in various social and professional settings. Addressing this critical issue head-on, our study embarks on a transformative journey to develop the Wearable Cleft Palate Speech Interpreter. The Researchers developed this device over a ten-month period, from August 2023 to May 2024, using agile methodologies, prototyping methods, and descriptive research, as well as the power of deep learning and neural network algorithms implemented in Python programming to achieve their objectives. Prototype testing, confusion matrix analysis, feedback questionnaires, and extensive internet research formed the foundation of the researchers' comprehensive data collection approach. The study was conducted at the University of the Assumption. The researchers' findings highlight the remarkable efficacy of the Wearable Cleft Palate Speech Interpreter, achieving a 93% accuracy rate in testing and 82% in the actual prototype and 100% precision in speech interpretation. The developed Wearable Cleft Palate Speech Interpreter achieved a grand mean of 3.43 from end-users and 3.67 from professionals on acceptability. The device is considered very acceptable by both professionals and end-users; thus, they concluded that it can be used to serve its intended purpose. This study can be subjected to further development and improvement by future researchers.