Joint information from nonlinear and linear features for spoofing detection: An i-vector/DNN based approach
Chunlei Zhang, Shivesh Ranjan, Mahesh Kumar Nandwana, Qian Zhang, Abhinav Misra, Gang Liu, Finnian Kelly, John H. L. Hansen
Abstract
Sustaining automatic speaker verification(ASV) systems from spoofing attacks remains an essential challenge, even if significant progress in ASV has been achieved in recent years. In this study, an automatic spoofing detection approach using an i-vector framework is proposed. Two approaches are used for frame-level feature extraction: cepstral-based Perceptual Minimum Variance Distortionless Response (PMVDR), and non-linear speech-production-motivated Teager Energy Operator (TEO) Critical Band (CB) Autocorrelation Envelope (Auto-Env). An utterance-level i-vector for each recording is formed by concatenating PMVDR and TEO-CB-Auto-Envi-vectors, followed by linear discriminative analysis (LDA) for maximizing the ratio of between-class to within-class scatterings. A Gaussian classifier and DNN are also investigated for back-end scoring. Experiments using the ASVspoof 2015 corpus show that our proposed method successfully detects spoofing attacks. By combining the TEO-CB-Auto-Env and PMVDR features, a relative 76.7% improvement in terms of EER is obtained compared with the best single-feature system.
BibTeX
@inproceedings{icassp2016_jointinformation,
title = {Joint information from nonlinear and linear features for spoofing detection: An i-vector/DNN based approach},
author = {Chunlei Zhang and Shivesh Ranjan and Mahesh Kumar Nandwana and Qian Zhang and Abhinav Misra and Gang Liu and Finnian Kelly and John H. L. Hansen},
booktitle = {ICASSP 2016},
year = {2016}
}