Library Services | Technical University of Kenya

Your cart is empty.
Normal view MARC view ISBD view

Privacy-Preserving Machine Learning for Speech Processing [electronic resource] / by Manas A. Pathak.

By: Pathak, Manas A [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Springer Theses, Recognizing Outstanding Ph.D. Research: Publisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: XVII, 141 p. 21 illus., 13 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461446392.Subject(s): Engineering | Data structures (Computer science) | Telecommunication | Production of electric energy or power | Engineering | Signal, Image and Speech Processing | Communications Engineering, Networks | Data Structures, Cryptology and Information Theory | Power Electronics, Electrical Machines and NetworksDDC classification: 621.382 Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

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.

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.

There are no comments for this item.

Log in to your account to post a comment.

2017 | The Technical University of Kenya Library | +254(020) 2219929, 3341639, 3343672 | library@tukenya.ac.ke | Haile Selassie Avenue