Paper Info
Title
Compressed high dimensional features for speaker spoofing detection.
Abstract
The vulnerability in Automatic Speaker Verification (ASV) systems to spoofing attacks such as speech synthesis (SS) and voice conversion (VC) has been recently proved. High-dimensional magnitude and phase based features possess outstanding spoofing detection performance but are not compatible with the Gaussian Mixture Model (GMM) classifiers which are commonly deployed in speaker recognition systems. In this paper, a Compressed Sensing (CS) framework is initially combined with high-dimensional (HD) features and a derived CS-HD based feature is proposed. A standalone spoofing detector assembled with the GMM classifier is evaluated on the ASVspoof 2015 database. Two ASV systems integrated with the spoofing detector are also tested. For the separate detector, an equal error rate (EER) of 0.01% and 535% are reached on the evaluation set for known attack and unknown attack, respectively. While for the ASV systems, the best EERs of 0.02% and 5.26% are achieved. The proposed CS-HD feature can obtain similar results with lower dimension than other systems. This suggests that the verification system can be made more computationally efficient.
Year
Venue
Field
2017
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
High-definition video,Spoofing attack,Pattern recognition,Computer science,Word error rate,Feature extraction,Speaker recognition,Artificial intelligence,Detector,Compressed sensing,Mixture model
DocType
ISSN
Citations 
Conference
2309-9402
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Yuanjun Zhao1146.42
Roberto Togneri281448.33
Victor Sreeram327026.88