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Spoofing Detection from a Feature Representation Perspective


Authors: X. Tian, Z. Wu, X. Xiao, E. S. Chng, and H. Li
Title: Spoofing Detection from a Feature Representation Perspective
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 may be 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.
Keywords: Spoofing attack; Spoofing detection; Counter-measure; High-dimensional feature; Phase
Conference Name: 41st IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'16)
Location: Shanghai, China
Publisher: IEEE
Year: 2016
Accepted PDF File: Spoofing_Detection_from_a_Feature_Representation_Perspective_accepted.pdf
Permanent Link: http://dx.doi.org/10.1109/ICASSP.2016.7472051
Reference: X. Tian, Z. Wu, X. Xiao, E. S. Chng, and H. Li, “Spoofing detection from a feature representation perspective,” in Proceedings of the 41st IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’16). IEEE, March 2016, pp. 2119–2123.
bibtex: 
@inproceedings{LILY-c73, 
    author	= {Tian, Xiaohai and Wu, Zhizheng and Xiao, Xiong and Chng, Eng Siong and Li, Haizhou},
    title	= {Spoofing Detection from a Feature Representation Perspective},  
    booktitle	= {Proceedings of the 41st IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'16)}, 
    year		= {2016}, 
    month	= {March}, 
    pages	= {2119-2123}, 
    location	= {Shanghai, China},
    publisher	= {IEEE},
 }