Secure binary embeddings of front-end factor analysis for privacy preserving speaker verification

Remote speaker verification services typically rely on the system to have access to the users recordings, or features derived from them, and also a model of the users voice. This conventional scheme raises several privacy concerns. In this work, we address this privacy problem in the context of a speaker verification system using a factor analysis based front-end extractor, the so-called i-vectors. Speaker verification without exposing speaker data is achieved by transforming speaker i-vectors to bit strings in a way that allows the computation of approximate distances, instead of exact ones. The key to the transformation uses a hashing scheme known as Secure Binary Embeddings. Then, a modified SVM kernel permits operating on the i-vector hashes. Experiments on sub-sets of NIST SRE 2008 showed that the secure system yielded similar results as its non-private counterpart.