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Voice spoofing detection corpus for single and multi-order audio replays

机译：单次音频重放的语音欺骗检测语料库

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The evolution of modern voice-controlled devices (VCDs) has revolutionized the Internet of Things (IoT) and resulted in the increased realization of smart homes, personalization, and home automation through voice commands. These VCDs can be exploited in IoT driven environments to generate various spoofing attacks, including the chaining of replay attacks (i.e. multi-order replay attacks). Existing datasets like ASVspoof 2017, ASVspoof 2019, and ReMASC contain only first-order replay recordings (i.e. replayed once); therefore, they cannot offer evaluation of anti-spoofing algorithms capable of detecting multi-order replay attacks. Additionally, large-scale datasets like ASVspoof 2017 and ASVspoof 2019 do not capture the characteristics of microphone arrays, which are an essential characteristic of modern VCDs. Therefore, there exists a need for a diverse replay spoofing detection corpus that consists of multi-order replay recordings against bona fide voice samples. This paper presents a novel voice spoofing detection corpus (VSDC) to evaluate the performance of multi-order replay anti-spoofing methods. The proposed VSDC consists of first-order (i.e. replayed once) and second-order replay (i.e. replayed twice) samples against the bona fide audio recordings. We ensured to create a diverse replay spoofing detection corpus in terms of environments, recording and playback devices, speakers, configurations, replay scenarios, etc. More specifically, we used 35 microphones, 25 different recording configurations, and 60 different playback configurations for first- and second-order replays to generate a total of 14,050 samples belonging to 19 speakers. Additionally, the proposed VSDC can also be used to evaluate the performance of speaker verification systems in terms of independent speaker verification. To the best of our knowledge, this is the first publicly available replay spoofing detection corpus comprised of first and second-order replay samples. Experimental results signify the effectiveness of the proposed VSDC in terms of evaluating the performance of anti-spoofing methods under multi-order replay attacks and diverse conditions.
机译：现代语音控制设备（VCD）的演变已经彻底改变了物联网（物联网），并通过语音命令导致智能家庭，个性化和家庭自动化的实现增加。这些VCD可以在IOT驱动的环境中利用，以生成各种欺骗攻击，包括重放攻击的链接（即多阶重放攻击）。现有数据集如asvspoof 2017，asvspoof 2019，reamasc仅包含一阶重放录制（即重播一次）;因此，他们不能提供能够检测多阶重放攻击的防欺骗算法的评估。此外，诸如ASVSPOOF 2017和ASVSPOOF 2019等大规模数据集不会捕获麦克风阵列的特性，这是现代VCD的基本特征。因此，需要各种重放欺骗检测语料库，该语料库包括针对真正的语音样本的多阶重放录制。本文提出了一种新型语音欺骗检测语料库（VSDC），以评估多阶重放防欺骗方法的性能。该提议的VSDC由一阶（即重播一次）和二阶重放（即重放两次）对Bona FIDE音频录制的样本组成。我们确保在环境，录制和回放设备，扬声器，配置，重播方案等方面创建多样化的重播欺骗检测语料库。更具体地说，我们使用35个麦克风，25个不同的录制配置以及60个不同的播放配置和二阶重放，以产生属于19个扬声器的14,050个样本。此外，所提出的VSDC还可用于评估扬声器验证系统在独立扬声器验证方面的性能。据我们所知，这是第一次公开的重播欺骗检测语料库，由第一和二阶重播样本组成。实验结果在评估多阶重播攻击和多样化条件下评估抗欺骗方法的性能方面的效力。

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