Investigating Self-Supervised Front Ends for Speech Spoofing Countermeasures
Xin Wang, Junichi Yamagishi
Abstract
Self-supervised speech model is a rapid progressing research topic, and many pre-trained models have been released and used in various down stream tasks.For speech anti-spoofing, most countermeasures (CMs) use signal processing algorithms to extract acoustic features for classification.In this study, we use pre-trained self-supervised speech models as the front end of spoofing CMs.We investigated different back end architectures to be combined with the self-supervised front end, the effectiveness of fine-tuning the front end, and the performance of using different pre-trained self-supervised models.Our findings showed that, when a good pre-trained front end was fine-tuned with either a shallow or a deep neural network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on the ASVspoof 2015, 2021 LA, and 2021 deepfake test sets.A sub-band analysis further demonstrated that the CM mainly used the information in a specific frequency band to discriminate the bona fide and spoofed trials across the test sets.