A framework for secure speech recognition

Paris Smaragdis, Madhusudana Shashanka

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present a process which enables privacy-preserving speech recognition transactions between two parties. We assume one party with private speech data and one party with private speech recognition models. Our goal is to enable these parties to perform a speech recognition task using their data, but without exposing their private information to each other. We will demonstrate how using secure multiparty computation principles we can construct a system where this transaction is possible, and how this system is computationally and securely correct. The protocols described herein can be used to construct a rudimentary speech recognition system and can easily be extended for arbitrary audio and speech processing

Original languageEnglish (US)
Article number4156216
Pages (from-to)1404-1413
Number of pages10
JournalIEEE Transactions on Audio, Speech and Language Processing
Volume15
Issue number4
DOIs
StatePublished - May 1 2007
Externally publishedYes

Keywords

  • Gaussian mixture models
  • Hidden Markov model (HMM)
  • Secure multiparty computation (SMC)
  • Speech recognition

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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