Privacy-Preserving Speaker Identification Using Gaussian Mixture Models

Chapter
Part of the Springer Theses book series (Springer Theses)

Abstract

In this chapter we present a framework for privacy-preserving speaker identification using Gaussian mixture models (GMMs). As discussed in the previous chapter, we consider two parties, the client having the test speech sample, and the server having a set of speaker models who is interested in performing the identification. Our privacy constraints are that the server should not be able to observe the speech sample and the client should not be able to observe the speaker models.

References

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Carnegie Mellon UniversityPittsburghUSA

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