Speaker De-identification System using Autoencoders and Adversarial Training
This addresses privacy concerns for users of voice interfaces and assistants in the European Union, though it appears incremental as it builds on existing adversarial and autoencoder techniques.
The paper tackled the problem of speaker privacy in speech data by proposing a de-identification system using autoencoders and adversarial training to suppress speaker, gender, and accent information. The result showed that this combination increased the equal error rate of a speaker verification system while maintaining the intelligibility of the anonymized speech.
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.