The automatic speech recognition (ASR) system based on deep neural network is easy to be attacked by an adversarial example due to the vulnerability of neural network, which is a hot topic in recent years. The adversarial example does harm to the ASR system, especially if the common-dependent ASR goes wrong, it will lead to serious consequences. To improve the robustness and security of the ASR system, the defense method against adversarial examples must be proposed. Based on this idea, we propose an algorithm of devastation and detection on adversarial examples which can attack the current advanced ASR system. We choose advanced text-dependent and command-dependent ASR system as our target system. Generating adversarial examples by the OPT on text-dependent ASR and the GA-based algorithm on command-dependent ASR. The main idea of our method is input transformation of the adversarial examples. Different random intensities and kinds of noise are added to the adversarial examples to devastate the perturbation previously added to the normal examples. From the experimental results, the method performs well. For the devastation of examples, the original speech similarity before and after adding noise can reach 99.68%, the similarity of the adversarial examples can reach 0%, and the detection rate of the adversarial examples can reach 94%.

翻译：基于深心神经网络的自动语音识别系统很容易受到一个对抗性例子的攻击,因为神经网络是近年来一个热门话题。对抗性例子对ASR系统有害,特别是如果共同依赖的ASR系统出错,它将导致严重后果。为了提高ASR系统的稳健性和安全性,必须提议对抗对抗性例子的防御方法。基于这一想法,我们提议对能够攻击当前先进的ASR系统的敌对性例子进行破坏和探测的算法。我们选择了先进的依赖文本和指令依赖的ASR系统作为我们的目标系统。产生巴勒斯坦被占领土对依赖文本的ASR和基于GA的基于指令的ASR的对抗性例子。我们方法的主要想法是将对抗性例子投入到对抗性例子的转变中。不同的随机强度和噪音都添加到对抗性例子中去,使先前添加到正常例子的扰动。从实验结果看,方法很好地表现了。关于破坏的实例,最初的言论在依赖文本的ASR和基于GASR的计算方法之前和之后,可以达到类似的比例为99.68%。