A Framework for Secure Speech Recognition

Paris Smaragdis, Madhusudana V. S. Shashanka
2007 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  
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
more » ... s 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. Index Terms-Gaussian mixture models, hidden Markov model (HMM), secure multiparty computation (SMC), speech recognition. 1558-7916/$25.00 © 2007 IEEE Paris Smaragdis (SM'06) is a member of the research staff at Mitsubishi Electric Research Laboratories, Cambridge, MA. Prior to that position, he was at the Massachusetts Institute of Technology, Cambridge, where he completed his graduate and postdoctoral training. His interests are computational audition, scene analysis, and the intersection of machine learning with signal processing.
doi:10.1109/icassp.2007.367233 dblp:conf/icassp/SmaragdisS07 fatcat:ibsklfaeoff5vot3tawdds6hbe