Fake Speech

Fake speech, encompassing synthetically generated and manipulated audio, poses a significant threat to authenticity and trust in digital media. Current research focuses on developing robust detection methods, often employing deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and vision transformers, and exploring various data augmentation and adversarial training techniques to improve generalization and robustness against sophisticated attacks. The ability to reliably detect fake speech is crucial for mitigating the spread of misinformation, protecting against financial fraud, and ensuring the integrity of audio-based evidence in legal and forensic contexts.