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Privacy-Preserving Machine Learning for Speech Processing

Springer Theses

By (author) Manas A. Pathak
Format: Hardback
Publisher: Springer-Verlag New York Inc., New York, NY, United States
Published: 25th Oct 2012
Dimensions: w 156mm h 234mm d 11mm
Weight: 405g
ISBN-10: 1461446384
ISBN-13: 9781461446385
Barcode No: 9781461446385
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Synopsis
This thesis discusses the privacy issues in speech-based applications such as biometric authentication, surveillance, and external speech processing services. Author Manas A. Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification and speech recognition. The author also introduces some of the tools from cryptography and machine learning and current techniques for improving the efficiency and scalability of the presented solutions. Experiments with prototype implementations of the solutions for execution time and accuracy on standardized speech datasets are also included in the text. Using the framework proposed may now make it possible for a surveillance agency to listen for a known terrorist without being able to hear conversation from non-targeted, innocent civilians.

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