Privacy-Preserving Machine Learning for Speech Processing
This thesis discusses the privacy issues in speech-based applications, including biometric authentication, surveillance, and external speech processing services. Manas A. Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification, and speech recognition. The thesis introduces tools from cryptography and machine learning and current techniques for improving the efficiency and scalability of the presented solutions, as well…
Read More ↓Title Statements
- Full title :Privacy-Preserving Machine Learning for Speech Processing / by Manas A. Pathak.
Summary
This thesis discusses the privacy issues in speech-based applications, including biometric authentication, surveillance, and external speech processing services. Manas A. Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification, and speech recognition. The thesis introduces tools from cryptography and machine learning and current techniques for improving the efficiency and scalability of the presented solutions, as well as experiments with prototype implementations of the solutions for execution time and accuracy on standardized speech datasets. Using the framework proposed may make it possible for a surveillance agency to listen for a known terrorist, without being able to hear conversation from non-targeted, innocent civilians.
Read More ↓Edition Statements
- Publication Date:2013.
Publication Statements
- Publisher(s):Imprint, Springer, Springer New York
- Place of Publication:New York, NY
- Language:eng
- Publication Date:2013.
The Physical Object
- Description:1 online resource (XVII, 141 pages 21 illustrations, 13 illustrations in color.)
Edition Identifiers
- ISBN:1461446384, 9781461446385, 1461446392, 9781461446392
- oclc:on1044383142
- LC Classification:TK5102.9
- Dewey Decimal:621.382
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