Joint Speaker Verification and Antispoofing in the $i$ -Vector Space
| Autor: | Elie Khoury, Aleksandr Sizov, Tomi Kinnunen, Sébastien Marcel, Zhizheng Wu |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Spoofing attack
Biometrics Computer Networks and Communications Computer science Speech recognition Feature extraction 02 engineering and technology Speaker recognition 030507 speech-language pathology & audiology 03 medical and health sciences Discriminative model 0202 electrical engineering electronic engineering information engineering NIST 020201 artificial intelligence & image processing 0305 other medical science Safety Risk Reliability and Quality Subspace topology Natural language |
| Zdroj: | IEEE Transactions on Information Forensics and Security. 10:821-832 |
| ISSN: | 1556-6021 1556-6013 |
| DOI: | 10.1109/tifs.2015.2407362 |
| Popis: | Any biometric recognizer is vulnerable to spoofing attacks and hence voice biometric, also called automatic speaker verification (ASV), is no exception; replay, synthesis, and conversion attacks all provoke false acceptances unless countermeasures are used. We focus on voice conversion (VC) attacks considered as one of the most challenging for modern recognition systems. To detect spoofing, most existing countermeasures assume explicit or implicit knowledge of a particular VC system and focus on designing discriminative features. In this paper, we explore back-end generative models for more generalized countermeasures. In particular, we model synthesis-channel subspace to perform speaker verification and antispoofing jointly in the ${i}$ -vector space, which is a well-established technique for speaker modeling. It enables us to integrate speaker verification and antispoofing tasks into one system without any fusion techniques. To validate the proposed approach, we study vocoder-matched and vocoder-mismatched ASV and VC spoofing detection on the NIST 2006 speaker recognition evaluation data set. Promising results are obtained for standalone countermeasures as well as their combination with ASV systems using score fusion and joint approach. |
| Databáze: | OpenAIRE |
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