Springer Theses

Privacy-Preserving Machine Learning for Speech Processing

Authors: Pathak, Manas A.

  • Nominated as outstanding PhD thesis from Carnegie Mellon University
  • Develops an efficient computational framework, making it possible to create speech processing applications such as voice biometrics, mining and speech recognition that are privacy-preserving
  • Presents a technology solution, which would allow a user to utilize an IVR system without fear that the system could learn undesired information, such as gender or nationality, or be able to record and edit voice
see more benefits

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-1-4614-4639-2
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-1-4614-4638-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • ISBN 978-1-4899-9120-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
About this book

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.

About the authors

Dr. Manas A. Pathak received the BTech degree in computer science from Visvesvaraya National Institute of Technology, Nagpur, India, in 2006, and the MS and PhD degrees from the Language Technologies Institute at Carnegie Mellon University (CMU) in 2009 and 2012 respectively. He is currently working as a research scientist at Adchemy, Inc. His research interests include intersection of data privacy, machine learning, speech processing.

Table of contents (13 chapters)

  • Thesis Overview

    Pathak, Manas A.

    Pages 3-6

  • Speech Processing Background

    Pathak, Manas A.

    Pages 7-18

  • Privacy Background

    Pathak, Manas A.

    Pages 19-45

  • Overview of Speaker Verification with Privacy

    Pathak, Manas A.

    Pages 49-53

  • Privacy-Preserving Speaker Verification Using Gaussian Mixture Models

    Pathak, Manas A.

    Pages 55-66

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-1-4614-4639-2
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-1-4614-4638-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • ISBN 978-1-4899-9120-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Privacy-Preserving Machine Learning for Speech Processing
Authors
Series Title
Springer Theses
Copyright
2013
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4614-4639-2
DOI
10.1007/978-1-4614-4639-2
Hardcover ISBN
978-1-4614-4638-5
Softcover ISBN
978-1-4899-9120-1
Series ISSN
2190-5053
Edition Number
1
Number of Pages
XVIII, 142
Topics