Privacy-Preserving Machine Learning for Speech Processing [[electronic resource] /] / by Manas A. Pathak

• Autore: Pathak Manas A
• Pubblicazione: New York, NY : , : Springer New York : , : Imprint : Springer, , 2013
• Edizione: 1st ed. 2013.
• Descrizione fisica: 1 online resource (144 p.)
• Disciplina: 004.2/1; 621.3994
• Soggetto topico: Signal processing; Image processing; Speech processing systems; Electrical engineering; Data structures (Computer science); Power electronics; Signal, Image and Speech Processing; Communications Engineering, Networks; Data Structures and Information Theory; Power Electronics, Electrical Machines and Networks
• Note generali: "Doctoral thesis accepted by Carnegie Mellon University, USA".
• Nota di contenuto: 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 Identification 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Privacy-Preserving Machine Learning for Speech Processing
• ISBN: 1-283-91048-9; 1-4614-4639-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910437900603321
• Lo trovi qui: Univ. Federico II
• Serie: Springer Theses, Recognizing Outstanding Ph.D. Research, . 2190-5053