# A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification

arXiv: https://arxiv.org/abs/1706.02101 · HTML: https://mlsanity.com/paper/1706.02101/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [How Deep Are the Fakes? Focusing on Audio Deepfake: A Survey](https://mlsanity.com/paper/2111.14203/) (2021)

## Abstract (arXiv)

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.