Privacy-preserving machine learning for speech processing
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 identificat...
Enregistré dans:
| Hovedforfatter: | |
|---|---|
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
New York, NY :
Springer New York
2013.
Cham : Springer Nature |
| Serier: | Springer Theses, Recognizing Outstanding Ph.D. Research
|
| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Kommentar: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Privacy-Preserving Machine Learning for Speech Processing, Texte imprimé, 9781461446385 • Privacy-Preserving Machine Learning for Speech Processing, Texte imprimé, 9781461446408 • Privacy-Preserving Machine Learning for Speech Processing, Texte imprimé, 9781489991201 |
Indholdsfortegnelse:
- 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.