Spoofed Speech

Spoofed speech detection focuses on distinguishing genuine human speech from synthetic or manipulated audio, aiming to improve the security of speaker verification systems. Current research emphasizes developing robust models, often employing deep learning architectures like ResNets, and exploring feature extraction methods that leverage both time-domain and frequency-domain information, including probabilistic embeddings and self-supervised pretrained models. This field is crucial for enhancing security in various applications, from voice authentication to combating the spread of misinformation through deepfakes, and ongoing research seeks to improve model generalization and interpretability to address the challenges posed by increasingly sophisticated spoofing techniques.