A framework for secure speech recognition

被引:43
作者
Smaragdis, Paris [1 ]
Shashanka, Madhusudana
机构
[1] Mitsubishi Elect Res Labs, Cambridge, MA 02139 USA
[2] Boston Univ, Dept Cognit & Neural Syst, Boston, MA 02215 USA
[3] Boston Univ, Hearing Res Ctr, Boston, MA 02215 USA
来源
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING | 2007年 / 15卷 / 04期
关键词
Gaussian mixture models; hidden Markov model (HMM); secure multiparty computation (SMC); speech recognition;
D O I
10.1109/TASL.2007.894526
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
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.
引用
收藏
页码:1404 / 1413
页数:10
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