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.
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© 2013 Springer Science+Business Media New York
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Pathak, M.A. (2013). Privacy-Preserving Speaker Identification Using Gaussian Mixture Models. In: Privacy-Preserving Machine Learning for Speech Processing. Springer Theses. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4639-2_8
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DOI: https://doi.org/10.1007/978-1-4614-4639-2_8
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