论文标题
反企业系统中的数据集人工制品：ASVSPOOF 2017基准测试的案例研究
Dataset artefacts in anti-spoofing systems: a case study on the ASVspoof 2017 benchmark
论文作者
论文摘要
自动扬声器验证欺骗和对策挑战促使研究促使研究言语生物识别系统免受各种不同的访问攻击。 2017年版的重点是重播欺骗攻击，并涉及参与者在提供的数据集中建立和培训系统（ASVSPOOF 2017）。到目前为止，该数据集已经发表了60多篇研究论文，但是没有人试图回答为什么对策在检测欺骗攻击方面似乎很成功。本文展示了数据集固有的人工制品如何为已发布系统的明显成功做出贡献。我们首先检查ASVSPOOF 2017数据集，并总结数据集中存在的各种文物。其次，我们演示了对策模型如何利用这些文物在此数据集中显得成功。第三，对于此数据集上的可靠和稳健的绩效估算，我们建议在训练和推理期间抛弃语音发言之前和之后的非语言细分和沉默。我们在数据集中创建语音启动和终点注释，并演示使用它们如何帮助对策模型变得不那么脆弱，因为它使用数据集中发现的伪像被操纵。最后，我们为框架级别和话语级模型提供了几个新的基准结果，这些结果可以用作该数据集的新基准。
The Automatic Speaker Verification Spoofing and Countermeasures Challenges motivate research in protecting speech biometric systems against a variety of different access attacks. The 2017 edition focused on replay spoofing attacks, and involved participants building and training systems on a provided dataset (ASVspoof 2017). More than 60 research papers have so far been published with this dataset, but none have sought to answer why countermeasures appear successful in detecting spoofing attacks. This article shows how artefacts inherent to the dataset may be contributing to the apparent success of published systems. We first inspect the ASVspoof 2017 dataset and summarize various artefacts present in the dataset. Second, we demonstrate how countermeasure models can exploit these artefacts to appear successful in this dataset. Third, for reliable and robust performance estimates on this dataset we propose discarding nonspeech segments and silence before and after the speech utterance during training and inference. We create speech start and endpoint annotations in the dataset and demonstrate how using them helps countermeasure models become less vulnerable from being manipulated using artefacts found in the dataset. Finally, we provide several new benchmark results for both frame-level and utterance-level models that can serve as new baselines on this dataset.
