A framework for secure speech recognition

Paris Smaragdis, Madhusudana V.S. Shashanka

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

Abstract

We present an algorithm that enables privacy-preserving speech recognition transactions between multiple parties. We assume two commonplace scenarios. One being the case where one of two parties has private speech data to be transcribed and the other party has private models for speech recognition. And the other being that of one party having a speech model to be trained using private data of multiple other parties. In both of the above cases data privacy is desired from both the data and the model owners. In this paper we will show how such collaborations can be performed while ensuring no private data leaks using secure multiparty computations. In neither case will any party obtain information on other parties data. The protocols described herein can be used to construct rudimentary speech recognition systems and can be easily extended for arbitrary audio and speech processing.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesIV969-IV972
DOIs
StatePublished - Aug 6 2007
Externally publishedYes
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/TerritoryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Cryptography
  • Data security
  • Speech recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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