ISCApad Archive » 2020 » ISCApad #267 » Journals » Computer, Speech and Language, special issue on Voice Privacy |
ISCApad #267 |
Thursday, September 10, 2020 by Chris Wellekens |
COMPUTER SPEECH AND LANGUAGE
Special issue on Voice Privacy
Deadline: January 8, 2021
Recent years have seen mounting calls for the preservation of privacy when treating personal data. Speech falls within that scope because it encapsulates a wealth of personal information that can be revealed by listening or by automatic speech analysis and recognition systems. This includes, e.g., age, gender, ethnic origin, geographical background, health or emotional state, political orientations, and religious beliefs, among others. In addition, speaker recognition systems can reveal the speaker?s identity. It is thus of no surprise that efforts to develop privacy preservation solutions for speech technology are starting to emerge.
A few studies have tackled the formal definition of privacy preservation, the provision of suitable datasets, and the design of evaluation protocols and metrics based on user and attacker models. Other studies have addressed the development of privacy preservation methods which maximize the utility for users while defeating attackers. Current methods fall into four categories: deletion, encryption, anonymization, and distributed learning. Deletion methods aim to delete or obfuscate speech based on speech enhancement or privacy-preserving feature extraction for ambient sound analysis purposes. Encryption methods such as fully homomorphic encryption and secure multiparty computation can be used to implement all computations in the encrypted domain. Anonymization methods aim to suppress personal information but retain other information by means of noise addition, speech transformation, voice conversion, speech synthesis, or adversarial learning. Decentralized or federated learning methods aim to learn models (for, e.g., keyword spotting) from distributed data without accessing individual data points nor leaking information about them in the models.
This special issue solicits papers describing advances in privacy protection for speech processing systems, including theoretical developments, algorithms or systems. Examples of topics relevant to the special issue include (but are not limited to):
Submission instructions:
Manuscript submissions shall be made through: https://www.editorialmanager.com/YCSLA/. Important dates:
January 8, 2021: Paper submission Guest Editors:
Emmanuel Vincent, Inria Natalia Tomashenko, Avignon Université Junichi Yamagishi, National Institute of Informatics and University of Edinburgh Nicholas Evans, EURECOM Paris Smaragdis, University of Illinois at Urbana-Champaign Jean-François Bonastre, Avignon Université |
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