Privacy-Preserving Speaker Verification and Identification Using Gaussian Mixture Models

Abstract

Speech being a unique characteristic of an individual is widely used in speaker verification and speaker identification tasks in applications such as authentication and surveillance respectively. In this article, we present frameworks for privacy-preserving speaker verification and speaker identification systems, where the system is able to perform the necessary operations without being able to observe the speech input provided by the user. In a speech-based authentication setting, this privacy constraint protect against an adversary who can break into the system and use the speech models to impersonate legitimate users. In surveillance applications, we require the system to first identify if the speech recording belongs to a suspect while preserving the privacy constraints. This prevents the system from listening in on conversations of innocent individuals. In this paper we formalize the privacy criteria for the speaker verification and speaker identification problems and construct Gaussian mixture model-based protocols. We also report experiments with a prototype implementation of the protocols on a standardized dataset for execution time and accuracy.