Lessons Learned from ASVSpoof and Remaining Challenges

Lessons Learned from ASVSpoof and Remaining Challenges

연구 분야: Strategies

학회: DDAM '22: Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia

Although speech technology reproducing an individual's voice is expected to bring new value to entertainment, it may cause security problems in speaker recognition systems if misused. In addition, there is a possibility of this technology being used for telephone fraud and information manipulation. Recognizing the importance of this issue, we have been working on speech anti-spoofing countermeasures since 2010, including building large-scale speech databases and organizing a series of ASVspoof challenges to evaluate the detectors on the shared database. This presentation will summarize the essential findings and lessons we have learned recently [1] and present the remaining challenges we are currently facing and the results we have achieved to date [2-4]. Examples of the lessons include a) sensitivity to hyper-parameters and features in deep learning-based countermeasure models and the importance of designing a network structure and learning loss that are stable even under different conditions, and b) effectiveness of ensemble learning of multiple models trained on different types of acoustic features and ineffectiveness of ensemble learning of different network structures using similar acoustic features. The ongoing research topics include 1) front-end features that are robust to domain and channel mismatches [2], 2) how to automatically expand the countermeasure database in a situation where new speech synthesis methods are being invented regularly [3], and 3) detection of partial synthetic regions to provide evidence for XAI anti-spoofing countermeasures [4]. Through these new attempts, the importance of studying the issue of speech anti-spoofing countermeasures from various angles, in addition to reducing EERs, will be illustrated.