Development of a dedicated software for the treatment of infants with phonological disorder, through the application of natural language processing algorithms. - Aquino Ruiz, Juan José (2025)
Limited access to speech therapy in rural areas of Ecuador affects children's phonological development. This study presents "FoneKid", a system based on natural language processing (NLP) that utilizes recurrent convolutional neural networks (CRNN) for speech recognition and deep neural network-based models specialized in phonological processing for phonological analysis. With 94% accuracy, the system dynamically adapts exercises using reinforcement learning. Developed in Unity and supported by Google cloud storage, "FoneKid" enables real-time evaluation and generates intelligent feedback based on phonetic patterns. Its modular architecture optimizes processing on mobile devices with intermittent internet access. Gamification enhances child retention, achieving a 95% interaction rate and a usability score of 84.2 on the SUS Scale. Results from tests with 22 children demonstrate significant improvements in phonetic articulation, with a 30% increase in correct pronunciation after two weeks of use. "FoneKid" emerges as a scalable alternative for speech therapy in resource-limited environments, combining artificial intelligence and playful strategies to optimize phonological learning.