dc.contributor.author | Chettri, B | en_US |
dc.contributor.author | Mishra, S | en_US |
dc.contributor.author | Sturm, BL | en_US |
dc.contributor.author | Benetos, E | en_US |
dc.date.accessioned | 2020-01-09T10:20:07Z | |
dc.date.issued | 2018-05-22 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/62299 | |
dc.description | 6 pages | en_US |
dc.description | 6 pages | en_US |
dc.description | 6 pages | en_US |
dc.description.abstract | The second Automatic Speaker Verification Spoofing and Countermeasures challenge (ASVspoof 2017) focused on "replay attack" detection. The best deep-learning systems to compete in ASVspoof 2017 used Convolutional Neural Networks (CNNs) as a feature extractor. In this paper, we study their performance in an end-to-end setting. We find that these architectures show poor generalization in the evaluation dataset, but find a compact architecture that shows good generalization on the development data. We demonstrate that for this dataset it is not easy to obtain a similar level of generalization on both the development and evaluation data. This leads to a variety of open questions about what the differences are in the data; why these are more evident in an end-to-end setting; and how these issues can be overcome by increasing the training data. | en_US |
dc.rights | This article is available under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. | |
dc.title | A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing | en_US |
dc.type | Report | |
dc.rights.holder | © The Author(s) 2018 | |
pubs.author-url | https://arxiv.org/abs/1805.09164v1 | en_US |
pubs.confidential | false | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | A Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineering | en_US |