Book Chapter in "Handbook of Biometric Anti-spoofing", Springer, S. Marcel, S. Li and M. Nixon, Eds., 2014
As with any task involving statistical pattern recognition, the assessment of
spoofi ng and anti-spoo ng approaches for voice recognition calls for signi cantscale
databases of spoofed speech signals. Depending on the application, these signals should normally reflect spoo ng attacks performed prior to acquisition at the sensor or microphone. Since the collection of large quantities of any biometric data is always extremely time consuming and cost prohibitive, and consistent with some telephony applications, almost all of the existing work to assess spoofing and anti-spoo ng in voice recognition is performed with databases of speech signals subjected to post-sensor spoo ng attacks. While such a setup can be justi ed, the lack of suitable sensor-level databases is a weakness in the research eld. Additionally, whereas spoo ng relates to authentication, and thus predominantly text-dependent scenarios, the majority of the related work involves
the use of text-independent databases better suited to surveillance applications than to authentication and spoo ng.
Type:
Book
Date:
2014-06-18
Department:
Digital Security
Eurecom Ref:
4439
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in Book Chapter in "Handbook of Biometric Anti-spoofing", Springer, S. Marcel, S. Li and M. Nixon, Eds., 2014 and is available at : http://dx.doi.org.10.1007/978-3-642-27733-7_9048-2
See also:
