Synthetic Speech Detector

Synthetic speech detection aims to distinguish artificially generated speech from human speech, addressing the growing concern of malicious use of realistic AI-generated audio. Current research focuses on developing robust detectors using deep learning architectures like Transformers and convolutional neural networks, often incorporating techniques such as attention mechanisms and feature fusion to improve accuracy and generalization across diverse datasets and speech synthesis methods. This field is crucial for combating audio deepfakes and misinformation, with ongoing efforts concentrating on improving detector robustness to compression, noise, and various synthesis techniques, as well as mitigating biases in detection algorithms.