Paper Info
Title
Replay Attack Detection Using Magnitude And Phase Information With Attention-Based Adaptive Filters
Abstract
Automatic Speech Verification ( ASV) systems are highly vulnerable to spoofing attacks, and replay attack poses the greatest threat among various spoofing attacks. In this paper, we propose a novel multi-channel feature extraction method with attention-based adaptive filters ( AAF). Original phase information, discarded by conventional feature extraction techniques after Fast Fourier Transform ( FFT), is promising in distinguishing genuine from replay spoofed speech. Accordingly, phase and magnitude information are respectively extracted as phase channel and magnitude channel complementary features in our system. First, we make discriminative ability analysis on full frequency bands with F-ratio methods. Then attention-based adaptive filters are implemented to maximize capturing of high discriminative information on frequency bands, and the results on ASVspoof 2017 challenge indicate that our proposed approach achieved relative error reduction rates of 78.7% and 59.8% on development and evaluation dataset than the baseline method.
Year
DOI
Venue
2019
10.1109/icassp.2019.8682739
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
replay attacks, phase information, frequency bands, adaptive filters, ASVspoof 2017
Frequency domain,Mel-frequency cepstrum,Spoofing attack,Pattern recognition,Computer science,Feature extraction,Fast Fourier transform,Adaptive filter,Artificial intelligence,Discriminative model,Replay attack
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Meng Liu13918.70
Longbiao Wang227244.38
Jianwu Dang329391.90
Seiichi Nakagawa4598104.03
Haotian Guan521.73
Xiangang Li65812.99