Privacy-Preserving Machine Learning for Speech Processing

Manas A. Pathak

ISBN 10: 1461446384 / ISBN 13: 9781461446385

Published by Springer New York, Springer New York, 2012

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781461446385

Bibliographic Details

• Title: Privacy-Preserving Machine Learning for Speech Processing
• Publisher: Springer New York, Springer New York
• Publication Date: 2012
• Language: English
• ISBN 10: 1461446384
• ISBN 13: 9781461446385
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 12D millimeters 241 millimeters width by 160 millimeters height by 12 millimeters depth
• Weight: 412 grams

About this title

Synopsis

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 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.