Name
Abstract | ||
|---|---|---|
In this paper, we aim to understand what makes replay spoofing detection difficult in the context of the ASVspoof 2017 corpus. We use FFT spectra, mel frequency cepstral coefficients (MFCC) and inverted MFCC (IMFCC) frontends and investigate different backends based on Convolutional Neural Networks (CNNs), Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs). On this database, we find that IMFCC frontend based systems show smaller equal error rate (EER) for high quality replay attacks but higher EER for low quality replay attacks in comparison to the baseline. However, we find that it is not straightforward to understand the influence of an acoustic environment (AE), a playback device (PD) and a recording device (RD) of a replay spoofing attack. One reason is the unavailability of metadata for genuine recordings. Second, it is difficult to account for the effects of the factors: AE, PD and RD, and their interactions. Finally, our frame-level analysis shows that the presence of cues (recording artefacts) in the first few frames of genuine signals (missing from replayed ones) influence class prediction. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MLSP.2018.8516968 | 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
Automatic speaker verification,spoofing detection,replay attack,spoofing countermeasure. | Mel-frequency cepstrum,Spoofing attack,Pattern recognition,Computer science,Convolutional neural network,Support vector machine,Word error rate,Fast Fourier transform,Artificial intelligence,Replay attack,Mixture model | Conference |
ISSN | ISBN | Citations |
1551-2541 | 978-1-5386-5478-1 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bhusan Chettri | 1 | 2 | 2.79 |
| Bob L. Sturm | 2 | 241 | 29.88 |
| Emmanouil Benetos | 3 | 557 | 52.48 |