Knowledge Commons of Institute of Automation,CAS
Continual Learning for Fake Audio Detection | |
Ma Haoxin1,2; Yi Jiangyan1; Tao Jianhua1,2,3; Bai Ye1,2; Tian Zhengkun1,2; Wang Chenglong1 | |
2021-09 | |
会议名称 | INTERSPEECH 2021 |
会议日期 | 2021-9 |
会议地点 | 线上(捷克) |
摘要 | Fake audio attack becomes a major threat to the speaker verification system. Although current detection approaches have achieved promising results on dataset-specific scenarios, they encounter difficulties on unseen spoofing data. Fine-tuning and retraining from scratch have been applied to incorporate new data. However, fine-tuning leads to performance degradation on previous data. Retraining takes a lot of time and computation resources. Besides, previous data are unavailable due to privacy in some situations. To solve the above problems, this paper proposes detecting fake without forgetting, a continual-learningbased method, to make the model learn new spoofing attacks incrementally. A knowledge distillation loss is introduced to loss function to preserve the memory of original model. Supposing the distribution of genuine voice is consistent among different scenarios, an extra embedding similarity loss is used as another constraint to further do a positive sample alignment. Experiments are conducted on the ASVspoof2019 dataset. The results show that our proposed method outperforms fine-tuning by the relative reduction of average equal error rate up to 81.62%. |
关键词 | fake audio detection continual learning detecting fake without forgetting |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48840 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
作者单位 | 1.NLPR, Institute of Automation, Chinese Academy of Sciences, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, China 3.CAS Center for Excellence in Brain Science and Intelligence Technology, China |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Ma Haoxin,Yi Jiangyan,Tao Jianhua,et al. Continual Learning for Fake Audio Detection[C],2021. |
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