Automated speech therapy through personalized pronunciation correction using reinforcement learning and large language models

Traditional approaches to pronunciation correction often face challenges in personalization, adaptability, and consistent feedback. This study introduces a novel AI-powered system that integrates Reinforcement Learning (RL) and Large Language Models (LLMs) to address these limitations. The system employs a custom Proximal Policy Optimization (PPO) algorithm for precise pronunciation evaluation and an Large Language Models to deliver detailed, encouraging, and user-specific feedback. It was evaluated using the CMU Sphinx Dictionary dataset, a foundational phonetic resource, alongside dynamically generated user-specific session data for personalized feedback and model refinement. Further validation utilized datasets such as TIMIT, LibriTTS, SpeechOcean762, and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), enabling direct comparisons with contemporary methods. Results demonstrate the system's robustness in handling diverse phonetic variations. While primarily tested on English data, its modular architecture supports adaptation to other languages and dialects through language-specific phonetic datasets. The system achieved exceptional performance metrics: 97.9 % phoneme-level accuracy, 87.7 % word-level accuracy, 95.2 % syllable count accuracy, and 89.4 % perfect accuracy on the CMU Sphinx dataset. This innovative approach underscores the potential of advanced AI techniques to enhance the personalization and effectiveness of pronunciation correction systems. All findings are quantitatively validated and thoroughly documented.