Speech communication systems play a critical role in modern digital applications including telecommunications, hearing aids, voice assistants, teleconferencing, healthcare monitoring, and human-computer interaction. However, speech signals are often degraded by environmental noise, reverberation, channel distortions, and interference, significantly affecting speech intelligibility and communication quality. Traditional speech enhancement techniques based on spectral filtering and statistical signal processing methods have shown limitations in highly dynamic acoustic environments. Recent advancements in Artificial Intelligence (AI), Deep Learning, and Digital Signal Processing (DSP) have enabled the development of intelligent speech enhancement systems capable of effectively suppressing noise while preserving speech quality. This paper presents a comprehensive study of Real-Time Speech Enhancement Techniques and proposes an Artificial Intelligence-Based Speech Enhancement Framework (AI-SEF) designed to improve speech clarity, intelligibility, and communication performance. The proposed framework integrates adaptive noise suppression, deep neural network-based speech reconstruction, real-time signal processing, and intelligent acoustic environment analysis. Experimental evaluation demonstrates substantial improvements in Signal-to-Noise Ratio (SNR), speech quality, perceptual evaluation scores, and computational efficiency compared with conventional speech enhancement approaches. The findings indicate that AI-driven speech enhancement technologies will become fundamental components of future intelligent communication systems, smart devices, and next-generation speech applications.

Keywords— Speech Enhancement, Digital Signal Processing, Artificial Intelligence, Deep Learning, Noise Reduction, Real-Time Communication, Speech Signal Processing, Acoustic Intelligence.