Voice Anonymization

Voice anonymization aims to remove speaker-identifying information from speech while preserving intelligibility and other relevant acoustic features. Current research focuses on developing efficient, low-latency models, often employing deep learning architectures like autoencoders and generative adversarial networks, to achieve this disentanglement of speaker identity from linguistic content. This field is crucial for protecting privacy in various applications, from voice assistants and social media to medical diagnostics, where anonymization is needed to balance privacy concerns with the utility of speech data. Ongoing efforts concentrate on improving the naturalness and robustness of anonymized speech against re-identification attempts.