论文标题
对抗语音识别系统的对抗性攻击中的语言依赖性
Language Dependencies in Adversarial Attacks on Speech Recognition Systems
论文作者
论文摘要
自动语音识别（ASR）系统在我们的日常设备中普遍存在。它们容易受到对抗攻击的影响，在这种攻击中，操纵输入样本欺骗了ASR系统的识别。尽管已经分析了各种英语ASR系统的对抗示例，但没有语言间比较脆弱性分析。我们比较德国人和英语ASR系统的攻击性，以DeepSpeech为例。我们研究了一种语言模型之一是否比另一个语言模型更容易受到操纵。我们实验的结果表明，在成功产生对抗性示例所必需的计算工作方面，英语和德语之间的统计学意义显着差异。该结果鼓励在ASR的鲁棒性分析中进一步研究语言依赖性特征。
Automatic speech recognition (ASR) systems are ubiquitously present in our daily devices. They are vulnerable to adversarial attacks, where manipulated input samples fool the ASR system's recognition. While adversarial examples for various English ASR systems have already been analyzed, there exists no inter-language comparative vulnerability analysis. We compare the attackability of a German and an English ASR system, taking Deepspeech as an example. We investigate if one of the language models is more susceptible to manipulations than the other. The results of our experiments suggest statistically significant differences between English and German in terms of computational effort necessary for the successful generation of adversarial examples. This result encourages further research in language-dependent characteristics in the robustness analysis of ASR.
