期刊
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
卷 10, 期 4, 页码 810-820出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2015.2398812
关键词
BIO-MODA-VOI; voice biometrics; anti-spoofing; phase information; synthetic speech detection
资金
- IKERBASQUE under Dr. Erro Research Fellowship
- Basque Government through Ber2Tek Project [IE12-333]
- Spanish Ministry of Economy and Competitiveness through SpeechTech4All Project [TEC2012-38939-C03-03]
- European Commission through Simple4All Project [287678]
In the field of speaker verification (SV) it is nowadays feasible and relatively easy to create a synthetic voice to deceive a speech driven biometric access system. This paper presents a synthetic speech detector that can be connected at the front-end or at the back-end of a standard SV system, and that will protect it from spoofing attacks coming from state-of-the-art statistical Text to Speech (TTS) systems. The system described is a Gaussian Mixture Model (GMM) based binary classifier that uses natural and copy-synthesized signals obtained from the Wall Street Journal database to train the system models. Three different state-of-the-art vocoders are chosen and modeled using two sets of acoustic parameters: 1) relative phase shift and 2) canonical Mel Frequency Cepstral Coefficients (MFCC) parameters, as baseline. The vocoder dependency of the system and multivocoder modeling features are thoroughly studied. Additional phase-aware vocoders are also tested. Several experiments are carried out, showing that the phase-based parameters perform better and are able to cope with new unknown attacks. The final evaluations, testing synthetic TTS signals obtained from the Blizzard challenge, validate our proposal.
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