Overview
- 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)
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Table of contents (13 chapters)
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Introduction
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Privacy-Preserving Speaker Verification
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Privacy-Preserving Speaker Identification
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Privacy-Preserving Speech Recognition
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Conclusion
Keywords
About this book
Authors and Affiliations
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
Book Title: Privacy-Preserving Machine Learning for Speech Processing
Authors: Manas A. Pathak
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-1-4614-4639-2
Publisher: Springer New York, NY
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Science+Business Media New York 2013
Hardcover ISBN: 978-1-4614-4638-5Published: 25 October 2012
Softcover ISBN: 978-1-4899-9120-1Published: 09 November 2014
eBook ISBN: 978-1-4614-4639-2Published: 26 October 2012
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XVIII, 142
Topics: Signal, Image and Speech Processing, Communications Engineering, Networks, Data Structures and Information Theory, Power Electronics, Electrical Machines and Networks