Skip to main content
SpringerLink
Log in
Book cover

Privacy-Preserving Machine Learning for Speech Processing

  • Book
  • © 2013

Overview

Authors:
  1. Manas A. Pathak

    1. Carnegie Mellon University, Pittsburgh, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • 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
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Theses (Springer Theses)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (13 chapters)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. Introduction

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Thesis Overview

      • Manas A. Pathak
      Pages 3-6

    3. Speech Processing Background

      • Manas A. Pathak
      Pages 7-18

    4. Privacy Background

      • Manas A. Pathak
      Pages 19-45

  3. Privacy-Preserving Speaker Verification

    1. Front Matter

      Pages 47-47
      Download chapter PDF

    2. Overview of Speaker Verification with Privacy

      • Manas A. Pathak
      Pages 49-53

    3. Privacy-Preserving Speaker Verification Using Gaussian Mixture Models

      • Manas A. Pathak
      Pages 55-66

    4. Privacy-Preserving Speaker Verification as String Comparison

      • Manas A. Pathak
      Pages 67-72

  4. Privacy-Preserving Speaker Identification

    1. Front Matter

      Pages 73-73
      Download chapter PDF

    2. Overview of Speaker Identification with Privacy

      • Manas A. Pathak
      Pages 75-78

    3. Privacy-Preserving Speaker Identification Using Gaussian Mixture Models

      • Manas A. Pathak
      Pages 79-87

    4. Privacy-Preserving Speaker Identification as String Comparison

      • Manas A. Pathak
      Pages 89-95

  5. Privacy-Preserving Speech Recognition

    1. Front Matter

      Pages 97-97
      Download chapter PDF

    2. Overview of Speech Recognition with Privacy

      • Manas A. Pathak
      Pages 99-102

    3. Privacy-Preserving Isolated-Word Recognition

      • Manas A. Pathak
      Pages 103-109

  6. Conclusion

    1. Front Matter

      Pages 111-111
      Download chapter PDF

    2. Thesis Conclusion

      • Manas A. Pathak
      Pages 113-116

    3. Future Work

      • Manas A. Pathak
      Pages 117-119

  7. Back Matter

    Pages 121-141
    Download chapter PDF
Back to top

Keywords

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.

Authors and Affiliations

  • Carnegie Mellon University, Pittsburgh, USA

    Manas A. Pathak

About the author

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.

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation