GAN and Diffusion Based Speech Enhancement

This article explores the application of Generative Adversarial Networks (GANs) and diffusion models for speech enhancement tasks, comparing their performance on both English and Turkish datasets. We evaluate the effectiveness of these models in removing background noise and improving speech intelligibility using metrics like Signal-to-Noise Ratio (SNR) and Perceptual Evaluation of Speech Quality (PESQ). By analyzing and comparing the results, we aim to shed light on the strengths and limitations of each approach in different language contexts, contributing to the advancement of speech enhancement techniques for multilingual applications.