Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes
This addresses security vulnerabilities in speaker verification systems against replay attacks, but it is incremental as it builds on existing datasets and methods.
The paper tackled replay attack spoofing detection in automatic speaker verification by using multi-task learning to classify replay noise, resulting in a 30% relative performance improvement on the ASVspoof2017 evaluation set.
In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multitask learning of noise classes. We define the noise that is caused by the replay attack as replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification. The multi-task learning includes classifying the noise of playback devices, recording environments, and recording devices as well as the spoofing detection. Each of the three types of the noise classes also includes a genuine class. The experiment results on the ASVspoof2017 datasets demonstrate that the performance of our proposed system is improved by 30% relatively on the evaluation set.