Phase Perturbation

Phase perturbation, the intentional alteration of the phase component of a signal (e.g., speech or oscillatory data), is a growing area of research focusing on improving the robustness and performance of various signal processing systems. Current work explores its application in data augmentation for machine learning models, particularly in speech recognition and anti-spoofing, and in enhancing the stability of predictions in complex dynamical systems. These studies often leverage neural network architectures and investigate the interplay between phase and magnitude information, aiming to improve model generalization and resilience to noise or channel distortions. The impact of this research extends to improving the accuracy and reliability of speech technologies and potentially to a broader understanding of complex systems dynamics.