  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. In order to
mitigate these weaknesses, in this paper, we propose a speaker
de-identification system based on adversarial training and autoencoders in
order to suppress speaker, gender, and accent information from speech.
Experimental results show that combining adversarial learning and autoencoders
increase the equal error rate of a speaker verification system while preserving
the intelligibility of the anonymized spoken content.
