Federico Alegre, Nicholas Evans, Tomi Kinnunen, Zhizheng Wu and Junichi Yamagishi
Book Chapter in "Handbook of Biometric Anti-spoofing", Springer, S. Marcel, S. Li and M. Nixon, Eds., 2014
Abstract: As with any task involving statistical pattern recognition, the assessment of spoofing and anti-spoofing approaches for voice recognition calls for significant scale databases of spoofed speech signals. Depending on the application, these signals should normally reflect spoofing 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-spoofing in voice recognition is performed with databases of speech signals subjected to post-sensor spoofing attacks. While such a setup can be justified, the lack of suitable sensor-level databases is a weakness in the research field. Additionally, whereas spoofing 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 spoofing.