Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …

This paper summarizes the applied deep learning practices in the field of speaker
recognition, both verification and identification. Speaker recognition has been a widely used …

The VoicePrivacy initiative aims to promote the development of privacy preservation tools for
speech technology by gathering a new community to define the tasks of interest and the …

Anonymisation has the goal of manipulating speech signals in order to degrade the
reliability of automatic approaches to speaker recognition, while preserving other aspects of …

Speech data conveys sensitive speaker attributes like identity or accent. With a small
amount of found data, such attributes can be inferred and exploited for malicious purposes …

The recently proposed x-vector based anonymization scheme converts any input voice into
that of a random pseudo-speaker. In this paper, we present a flexible pseudo-speaker …

Sharing real-world speech utterances is key to the training and deployment of voice-based
services. However, it also raises privacy risks as speech contains a wealth of personal data …

The rapid increase in web services and mobile apps, which collect personal data from users,
has also increased the risk that their privacy may be severely compromised. In particular, the …

The task of the challenge is to develop a voice anonymization system for speech data which
conceals the speaker's voice identity while protecting linguistic content and emotional states …

With the development of smart devices, such as the Amazon Echo and Apple's HomePod,
speech data have become a new dimension of big data. However, privacy and security …