Ebook: Privacy-Preserving Machine Learning for Speech Processing

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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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.

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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.
Content:
Front Matter....Pages i-xvii
Front Matter....Pages 1-1
Thesis Overview....Pages 3-6
Speech Processing Background....Pages 7-18
Privacy Background....Pages 19-45
Front Matter....Pages 47-47
Overview of Speaker Verification with Privacy....Pages 49-53
Privacy-Preserving Speaker Verification Using Gaussian Mixture Models....Pages 55-66
Privacy-Preserving Speaker Verification as String Comparison....Pages 67-72
Front Matter....Pages 73-73
Overview of Speaker Identification with Privacy....Pages 75-78
Privacy-Preserving Speaker Identification Using Gaussian Mixture Models....Pages 79-87
Privacy-Preserving Speaker Identification as String Comparison....Pages 89-95
Front Matter....Pages 97-97
Overview of Speech Recognition with Privacy....Pages 99-102
Privacy-Preserving Isolated-Word Recognition....Pages 103-109
Front Matter....Pages 111-111
Thesis Conclusion....Pages 113-116
Future Work....Pages 117-119
Back Matter....Pages 121-141

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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.
Content:
Front Matter....Pages i-xvii
Front Matter....Pages 1-1
Thesis Overview....Pages 3-6
Speech Processing Background....Pages 7-18
Privacy Background....Pages 19-45
Front Matter....Pages 47-47
Overview of Speaker Verification with Privacy....Pages 49-53
Privacy-Preserving Speaker Verification Using Gaussian Mixture Models....Pages 55-66
Privacy-Preserving Speaker Verification as String Comparison....Pages 67-72
Front Matter....Pages 73-73
Overview of Speaker Identification with Privacy....Pages 75-78
Privacy-Preserving Speaker Identification Using Gaussian Mixture Models....Pages 79-87
Privacy-Preserving Speaker Identification as String Comparison....Pages 89-95
Front Matter....Pages 97-97
Overview of Speech Recognition with Privacy....Pages 99-102
Privacy-Preserving Isolated-Word Recognition....Pages 103-109
Front Matter....Pages 111-111
Thesis Conclusion....Pages 113-116
Future Work....Pages 117-119
Back Matter....Pages 121-141
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