Privacy-Preserving Machine Learning for Speech Processing

AvManas A. Pathak

Inbunden, Engelska, 2012

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Häftad

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2012-10-25
Mått:155 x 235 x 12 mm
Vikt:412 g
Format:Inbunden
Språk:Engelska
Serie:Springer Theses
Antal sidor:142
Upplaga:2013
Förlag:Springer-Verlag New York Inc.
ISBN:9781461446385

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Mer om författaren

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.

Innehållsförteckning

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.