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

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 identificat...

Table of Contents:

• Thesis Overview
• Speech Processing Background
• Privacy Background
• Overview of Speaker Verification with Privacy
• Privacy-Preserving Speaker Verification Using Gaussian Mixture Models
• Privacy-Preserving Speaker Verification as String Comparison
• Overview of Speaker Indentification with Privacy
• Privacy-Preserving Speaker Identification Using Gausian Mixture Models
• Privacy-Preserving Speaker Identification as String Comparison
• Overview of Speech Recognition with Privacy
• Privacy-Preserving Isolated-Word Recognition
• Thesis Conclusion
• Future Work
• Differentially Private Gaussian Mixture Models.