Paper Info
Title
Linear prediction residual features for automatic speaker verification anti-spoofing
Abstract
Automatic speaker verification (ASV) systems are highly vulnerable against spoofing attacks. Anti-spoofing, determining whether a speech signal is natural/genuine or spoofed, is very important for improving the reliability of the ASV systems. Spoofing attacks using the speech signals generated using speech synthesis and voice conversion have recently received great interest due to the 2015 edition of Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2015). In this paper, we propose to use linear prediction (LP) residual based features for anti-spoofing. Three different features extracted from LP residual signal were compared using the ASVspoof 2015 database. Experimental results indicate that LP residual phase cepstral coefficients (LPRPC) and LP residual Hilbert envelope cepstral coefficients (LPRHEC) obtained from the analytic signal of the LP residual yield promising results for anti-spoofing. The proposed features are found to outperform standard Mel-frequency cepstral coefficients (MFCC) and Cosine Phase (CosPhase) features. LPRPC and LPRHEC features give the smallest equal error rates (EER) for eight spoofing methods out of ten spoofing attacks in comparison to MFCC and CosPhase features.
Year
DOI
Venue
2018
https://doi.org/10.1007/s11042-017-5181-0
Multimedia Tools Appl.
Keywords
Field
DocType
Speaker verification,Anti-spoofing,Countermeasure,Linear prediction residual
Speaker verification,Residual,Mel-frequency cepstrum,Analytic signal,Speech synthesis,Trigonometric functions,Pattern recognition,Spoofing attack,Computer science,Linear prediction,Speech recognition,Artificial intelligence
Journal
Volume
Issue
ISSN
77
13
1380-7501
Citations 
PageRank 
References 
1
0.35
23
Authors
1
Name
Order
Citations
PageRank
Cemal Hanilçi117111.23