استخدام تقنيات الذكاء الاصطناعي في تدريب الاطفال على النطق

Abstract

Speech is a fundamental component of linguistic and social communication in children, and when speech difficulties exist (such as phoneme distortion, omission, or substitution of sounds), early intervention helps prevent long-term educational and social consequences. This study aims to explore how artificial intelligence (AI) techniques—including speech recognition (ASR), natural language processing (NLP), and deep learning—can be used to design an interactive system for training and improving children's speech. The study includes: (1) collecting audio data from children of various ages speaking prepared words and sentences; (2) developing an AI model capable of analyzing speech and detecting errors with sufficient accuracy; (3) providing appropriate interactive feedback to the child and the teacher or parents; (4) conducting a pilot comparing an experimental group using the system with a control group using traditional training; and (5) analyzing the results from both a technical and educational perspective. The system is expected to demonstrate improved pronunciation accuracy, increased child engagement in training, and enhanced therapist or teacher tracking of progress. However, challenges remain, including the diversity of children's accents, the quality of audio recordings, and data protection ethics. In conclusion, integrating artificial intelligence into speech therapy for children is a promising avenue for expanding treatment options, but it requires careful design, clinical trials, and adaptation to diverse linguistic and cultural contexts.