ENGLISH

Privacy-Preserving Machine Learning for Speech Processing

By Manas A. Pathak (auth.)

Book information

Publisher
Springer-Verlag New York
Year
2013
ISBN
978-1-4614-4638-5, 978-1-4614-4639-2
DOI
10.1007/978-1-4614-4639-2
Language
english
Format
PDF
Filesize
3 MB (3185585 bytes)
Series
Springer Theses
Edition
1
Pages
142\144
Orientation
yes
Scanned
yes
Time added
2013-08-01 04:00:00
Cryptology and Information Theory; Power ElectroniElectrical Machines and NetworksImage and Speech Processing; Communications EngineNetworks; Data StructuresSignal

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.

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