Description: Privacy-Preserving Machine Learning for Speech Processing by Manas A. Pathak This thesis discusses the privacy issues in speech-based applications such as biometric authentication, surveillance, and external speech processing services. Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification and speech recognition. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Back Cover 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. Author Biography 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 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. Feature 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 Details ISBN1461446384 Author Manas A. Pathak Short Title PRIVACY-PRESERVING MACHINE LEA Series Springer Theses Language English ISBN-10 1461446384 ISBN-13 9781461446385 Media Book Format Hardcover Series Number 7 Year 2012 Imprint Springer-Verlag New York Inc. Place of Publication New York, NY Country of Publication United States DEWEY 621.3994 Pages 142 DOI 10.1007/978-1-4614-4639-2 AU Release Date 2012-10-25 NZ Release Date 2012-10-25 US Release Date 2012-10-25 UK Release Date 2012-10-25 Publisher Springer-Verlag New York Inc. Edition Description 2013 ed. Edition 2013th Publication Date 2012-10-25 Alternative 9781489991201 Audience Professional & Vocational Illustrations XVIII, 142 p. We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:96314704;
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Format: Hardcover
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Book Title: Privacy-Preserving Machine Learning for Speech Processing
