Speaker De-identification System using Autoencoders and Adversarial Training

Understand

The fast increase of web services and mobile apps, which collect personal data from users, increases the risk that their privacy may be severely compromised.

• In particular, the increasing variety of spoken language interfaces and voice assistants empowered by the vertiginous breakthroughs in Deep Learning are prompting important concerns in the European Union to preserve speech data privacy.
• For instance, an attacker can record speech from users and impersonate them to get access to systems requiring voice identification.
• Hacking speaker profiles from users is also possible by means of existing technology to extract speaker, linguistic (e.g., dialect) and paralinguistic features (e.g., age) from the speech signal.