Revisiting Speech Content Privacy

Jennifer Williams, Junichi Yamagishi, Paul-Gauthier Noé, Cassia Valentini-Botinhao, Jean-François Bonastre

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we discuss an important aspect of speech privacy: protecting spoken content. New capabilities from the field of machine learning provide a unique and timely opportunity to revisit speech content protection. There are many different applications of content privacy, even though this area has been under explored in speech technology research. This paper presents several scenarios that indicate a need for speech content privacy even as the specific techniques to achieve content privacy may necessarily vary. Our discussion includes several different types of content privacy including recoverable and non-recoverable content. Finally, we introduce evaluation strategies as well as describe some of the difficulties that may be encountered.
Original languageEnglish
Title of host publicationProceedings of the Symposium of the Security & Privacy in Speech Communication
Number of pages5
Publication statusAccepted/In press - 21 Sep 2021
Event1st ISCA Symposium of the Security & Privacy in Speech Communication - Virtual
Duration: 10 Nov 202112 Nov 2021
https://www.spsc-symposium2021.de/

Symposium

Symposium1st ISCA Symposium of the Security & Privacy in Speech Communication
Abbreviated titleSPSC 2021
Period10/11/2112/11/21
Internet address

Keywords

  • privacy
  • speech coding
  • speech recognition

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