[Keynote Talk] Lessons learned from ASVSpoof and remaining challenges

講演者：Junichi Yamagishi
会議名：1st International Workshop on Deepfake Detection for Audio Multimedia (DDAM 2022)
主催者：ACM Multimedia 2022
開催地：オンライン
開催日：2022年10月14日
URL : http://addchallenge.cn/ddam2022

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. 

[1] Xin Wang, Junichi Yamagishi “A Practical Guide to Logical Access Voice Presentation Attack Detection,” Frontiers in Fake Media Generation and Detection, Springer, May 2022 

[2] Xin Wang, Junichi Yamagishi “Investigating self-supervised front ends for speech spoofing countermeasures,” Odyssey 2022: The Speaker and Language Recognition Workshop, June 2022 

[3] Xin Wang, Junichi Yamagishi “Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure,” Work in progress April 2022 

[4] Lin Zhang, Xin Wang, Erica Cooper, Nicholas Evans, Junichi Yamagishi “The PartialSpoof Database and Countermeasures for the Detection of Short Generated Audio Segments Embedded in a Speech Utterance,” Submitted to IEEE/ACM Transactions on Audio Speech and Language Processing http://arxiv.org/abs/arXiv:2204.05177