A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification
This addresses security vulnerabilities in speaker verification systems against replay attacks, but it is incremental as it builds on existing anti-spoofing methods.
The study tackled the problem of replay attacks in automatic speaker verification systems by analyzing over-fitting risks from variability factors and proposing a frequency warping approach, achieving verification on the ASV-spoof 2017 database.
For practical automatic speaker verification (ASV) systems, replay attack poses a true risk. By replaying a pre-recorded speech signal of the genuine speaker, ASV systems tend to be easily fooled. An effective replay detection method is therefore highly desirable. In this study, we investigate a major difficulty in replay detection: the over-fitting problem caused by variability factors in speech signal. An F-ratio probing tool is proposed and three variability factors are investigated using this tool: speaker identity, speech content and playback & recording device. The analysis shows that device is the most influential factor that contributes the highest over-fitting risk. A frequency warping approach is studied to alleviate the over-fitting problem, as verified on the ASV-spoof 2017 database.