Intelligent Audio Processing System with Deep Reinforcement Learning for Personalized Speech Comprehension Enhancement

This paper presents an intelligent audio processing system that leverages deep reinforcement learning algorithms to optimize speech comprehension training through personalized content adaptation. The system integrates advanced signal processing techniques with multi-dimensional user modeling to dynamically adjust audio content parameters including playback speed, vocabulary complexity, and acoustic characteristics. Our approach employs a Deep Q-Network (DQN) architecture combined with collaborative filtering-enhanced neural networks to model user cognitive patterns and optimize learning trajectories in real-time. The system features a five-component modular architecture supporting automatic speech recognition (ASR), natural language processing (NLP), and behavioral analytics for comprehensive user profiling. Experimental validation with 320 participants over 12 weeks demonstrates significant performance improvements: 23.5% enhancement in comprehension accuracy, 35.8% improvement in processing efficiency, and 87.2% user satisfaction rate. The reinforcement learning agent successfully learns optimal content delivery strategies through continuous user interaction, achieving superior performance compared to traditional rule-based audio processing systems. Results indicate that AI-driven adaptive audio processing represents a promising approach for intelligent multimedia content delivery in educational technology applications.