4 research outputs found
Privacy-preserving Automatic Speaker Diarization
Automatic Speaker Diarization (ASD) is an enabling technology with numerous
applications, which deals with recordings of multiple speakers, raising special
concerns in terms of privacy. In fact, in remote settings, where recordings are
shared with a server, clients relinquish not only the privacy of their
conversation, but also of all the information that can be inferred from their
voices. However, to the best of our knowledge, the development of
privacy-preserving ASD systems has been overlooked thus far. In this work, we
tackle this problem using a combination of two cryptographic techniques, Secure
Multiparty Computation (SMC) and Secure Modular Hashing, and apply them to the
two main steps of a cascaded ASD system: speaker embedding extraction and
agglomerative hierarchical clustering. Our system is able to achieve a
reasonable trade-off between performance and efficiency, presenting real-time
factors of 1.1 and 1.6, for two different SMC security settings