Noise Augmentation

Noise augmentation, the process of adding noise to training data, is a widely used technique to improve the robustness and performance of various machine learning models, particularly in speech processing, image analysis, and multimodal learning. Current research focuses on optimizing noise augmentation strategies for specific applications, including adversarial robustness in speech recognition, low-resource scenarios in speech translation, and mitigating hallucinations in large language models. This technique's significance lies in its ability to enhance model generalization, reduce overfitting, and improve performance with limited training data, leading to more reliable and efficient AI systems across diverse domains.