Investigating Active-Learning-Based Training Data Selection for Speech Spoofing Countermeasure

Training a speech spoofing countermeasure (CM) that generalizes to various unseen test data is challenging. Methods such as data augmentation and self-supervised learning can help, but the imperfect CM performance still calls for additional strategies. This paper investigates CM training using active learning (AL) to select useful training data from a large pool set, which is an unexplored area for speech anti-spoofing. Existing AL methods are compared to select useful data from a large pool set. A new AL method is also proposed that actively removes useless data from a pool. Experiments demonstrate that an energy-score-based AL method and the proposed data-removing method outperformed our strong baseline, and the relative reduction in detection error rates was higher than 40% on multiple test sets. Furthermore, compared with a top-line method that blindly used the whole pool set for training, the two AL-based CMs used less training data and achieved better or similar performance.