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|Title:||Spoofing detection from a feature representation perspective||Authors:||Tian, Xiaohai
Chng, Eng Siong
DRNTU::Engineering::Computer science and engineering
|Issue Date:||2016||Source:||Tian, X., Wu, Z., Xiao, X., Chng, E. S., & Li, H. (2016). Spoofing detection from a feature representation perspective. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2119-2123. doi:10.1109/ICASSP.2016.7472051||Abstract:||Spoofing detection, which discriminates the spoofed speech from the natural speech, has gained much attention recently. Low-dimensional features that are used in speaker recognition/verification are also used in spoofing detection. Unfortunately, they don't capture sufficient information required for spoofing detection. In this work, we investigate the use of high-dimensional features for spoofing detection, that maybe more sensitive to the artifacts in the spoofed speech. Six types of high-dimensional feature are employed. For each kind of feature, four different representations are extracted, i.e. the original high-dimensional feature, corresponding low-dimensional feature, the low- and the high-frequency regions of the original high-dimensional feature. Dynamic features are also calculated to assess the effectiveness of the temporal information to detect the artifacts across frames. A neural network-based classifier is adopted to handle the high-dimensional features. Experimental results on the standard ASVspoof 2015 corpus suggest that high-dimensional features and dynamic features are useful for spoofing attack detection. A fusion of them has been shown to achieve 0.0% the equal error rates for nine of ten attack types.||URI:||https://hdl.handle.net/10356/89643
|DOI:||10.1109/ICASSP.2016.7472051||Rights:||© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICASSP.2016.7472051].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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