论文标题
调查基于积极学习的培训数据选择，以进行语音欺骗对策
Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure
论文作者
论文摘要
需要培训欺骗对策（CM），该策略概括为各种看不见的数据是必需的，但具有挑战性。虽然适用于数据增强和自我监督学习等方法，但各种测试集的CM不完美的CM性能仍要求采取其他策略。这项研究采取了主动性，并使用主动学习（AL）研究了CM培训，该框架迭代地从大型池组中选择有用的数据并通过CM进行微调。这项研究比较了一些测量数据有用性的方法以及使用从各种来源收集的不同池集的影响。结果表明，基于AL的CM的概括比我们在多个测试测试中的强基线更好。此外，与仅将整个数据池进行培训的顶级CM相比，基于AL的CMS使用较少的培训数据实现了相似的性能。尽管没有找到AL的单一最佳配置，但经验法则是在池集中包含各种欺骗和善意数据，并避免选择CM感到自信的数据的任何AL数据选择方法。
Training a spoofing countermeasure (CM) that generalizes to various unseen data is desired but challenging. While methods such as data augmentation and self-supervised learning are applicable, the imperfect CM performance on diverse test sets still calls for additional strategies. This study took the initiative and investigated CM training using active learning (AL), a framework that iteratively selects useful data from a large pool set and fine-tunes the CM. This study compared a few methods to measure the data usefulness and the impact of using different pool sets collected from various sources. The results showed that the AL-based CMs achieved better generalization than our strong baseline on multiple test tests. Furthermore, compared with a top-line CM that simply used the whole data pool set for training, the AL-based CMs achieved similar performance using less training data. Although no single best configuration was found for AL, the rule of thumb is to include diverse spoof and bona fide data in the pool set and to avoid any AL data selection method that selects the data that the CM feels confident in.
