Anti-Spoofing System: An Investigation of Measures to Detect Synthetic and Human Speech

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Anti-Spoofing System: An Investigation of Measures to Detect Synthetic and Human Speech

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Title: Anti-Spoofing System: An Investigation of Measures to Detect Synthetic and Human Speech
Author(s):
Misra, A.;
Ranjan, S.;
Zhang, C.;
Hansen, John H. L. (UT Dallas)
Item Type: article
Keywords: Anti-spoofing
Countermeasures
Speaker verification
Abstract: Automatic Speaker Verification (ASV) systems are prone to spoofing attacks of various kinds. In this study, we explore the effects of different features and spoofing algorithms on a state-of-the-art i-vector speaker verification system. Our study is based on the standard dataset and evaluation protocols released as part of the ASVspoof 2015 challenge. We compare how different features perform while detecting both genuine and spoofed speech. We observe that features that contain phase information (Modified Group Delay based features) are better in detecting synthetic speech, and give comparable performance when compared to standard MFCCs. We report an anti-spoofing system that performs well both on known as well as unknown spoofing attacks.
Publisher: International Speech and Communication Association
ISSN: 2308-457X
Persistent Link: http://hdl.handle.net/10735.1/5060
Bibliographic Citation: Misra, A., S. Ranjan, C. Zhang, and J. H. L. Hansen. 2015. "Anti-spoofing system: An investigation of measures to detect synthetic and human speech." INTERSPEECH 2015 (16th Annual Conference of the International Speech Communication Association) p. 3466-3470.
Terms of Use: ©2015 ISCA
Sponsors: Partially funded by AFRL contract FA8750-12-1-0188

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