Privacy-Preserving Machine Learning for Speech Processing

• Main Author: Pathak, Manas A. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: New York, NY : Springer New York : Imprint: Springer, 2013.
• Series: Springer Theses, Recognizing Outstanding Ph.D. Research,
• Subjects: Engineering.; Data structures (Computer science).; Telecommunication.; Production of electric energy or power.; Signal, Image and Speech Processing.; Communications Engineering, Networks.; Data Structures, Cryptology and Information Theory.; Power Electronics, Electrical Machines and Networks.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-4639-2
• Physical Description: XVII, 141 p. 21 illus., 13 illus. in color. online resource.
• ISBN: 9781461446392
• ISSN: 2190-5053

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.