Privacy-Preserving Speaker Identification as String Comparison

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

Abstract

In this chapter, we present a framework for speaker identification using locality sensitive hashing (LSH) and supervectors. Instead of GMM evaluation that we considered in the previous chapter, we transform the problem of speaker identification into string comparison. The motivation behind doing so is to perform speaker identification with privacy while having minimal computational overhead. Similar to the speaker verification framework of Chapter 6, we convert the utterances into supervector features [CampbellSRS et al., 2006] that are invariant with the length of the utterance. By applying the LSH transformation to the supervectors, we reduce the problem of nearest-neighbor classification into string comparison. As LSH is not privacy-preserving, we apply the cryptographic hash function to the LSH keys. This allows us to check if two LSH keys match without knowing their contents.

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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