dc.contributor.author | Mandalapu, Hareesh | |
dc.contributor.author | Busch, Christoph | |
dc.contributor.author | Ramachandra, Raghavendra | |
dc.date.accessioned | 2021-03-30T07:08:40Z | |
dc.date.available | 2021-03-30T07:08:40Z | |
dc.date.created | 2021-03-19T10:16:24Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-981-13-6052-7 | |
dc.identifier.uri | https://hdl.handle.net/11250/2736050 | |
dc.description.abstract | Well-known vulnerabilities of voice-based biometrics are impersonation, replay attacks, artificial signals/speech synthesis, and voice conversion. Among these, voice impersonation is the obvious and simplest way of attack that can be performed. Though voice impersonation by amateurs is considered not a severe threat to ASV systems, studies show that professional impersonators can successfully influence the performance of the voice-based biometrics system. In this work, we have created a novel voice impersonation attack dataset and studied the impact of voice impersonation on automatic speaker verification systems. The dataset consisting of celebrity speeches from 3 different languages, and their impersonations are acquired from YouTube. The vulnerability of speaker verification is observed among all three languages on both the classical i-vector based method and the deep neural network-based x-vector method. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Proceeding of the 3rd International Conference on Intelligent Technologies and Applications (INTAP) | |
dc.title | Multilingual Voice Impersonation Dataset and Evaluation | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.identifier.doi | 10.1007/978-3-030-71711-7_15 | |
dc.identifier.cristin | 1899213 | |
dc.description.localcode | This is a post-peer-review, pre-copyedit version of an article. The final authenticated version is available online at: http://dx.doi.org/https://doi.org/10.1007/978-3-030-71711-7_15 | en_US |
cristin.ispublished | false | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |