Speaker Anonymization

Speaker anonymization aims to remove identifying information from speech recordings while preserving linguistic content and other relevant features like emotion. Current research heavily utilizes deep learning models, particularly autoencoders and generative adversarial networks (GANs), often incorporating disentanglement techniques to separate speaker identity from other speech characteristics, with some work exploring signal processing methods and latent space transformations. This field is crucial for protecting user privacy in applications involving voice data, impacting areas like healthcare, voice assistants, and forensic speech analysis by enabling the use of speech data while mitigating privacy risks.