Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (14): 1-15.DOI: 10.3778/j.issn.1002-8331.2202-0196

• Research Hotspots and Reviews • Previous Articles     Next Articles

Survey on Adversarial Example Attack and Defense Technology for Automatic Speech Recognition

LI Kezi, XU Yang, ZHANG Sicong, YAN Jiale   

  1. Key Laboratory of Information and Computing Science of Guizhou Province, Guizhou Normal University, Guiyang 550001, China
  • Online:2022-07-15 Published:2022-07-15



  1. 贵州师范大学 贵州省信息与计算科学重点实验室,贵阳 550001

Abstract: Speech recognition technology is an important way of human-computer interaction. With the continuous development of deep learning, automatic speech recognition system based on deep learning has also made important progress. However, well-designed audio adversarial examples can cause errors in the automatic speech recognition system based on neural network, and bring security risks to the application of combined speech recognition system. In order to improve the security of automatic speech recognition system based on neural network, it is necessary to study the attack and defense of audio adversarial examples. Firstly, the research status of adversarial examples generation and defense technology is analyzed and summarized. Then automatic speech recognition system audio adversarial examples attack and defense techniques and related challenges and solutions are introduced.

Key words: automatic speech recognition, deep learning, adversarial attack, adversarial defense

摘要: 语音辨识技术是人机交互的重要方式。随着深度学习的不断发展,基于深度学习的自动语音辨识系统也取得了重要进展。然而,经过精心设计的音频对抗样本可以使得基于神经网络的自动语音辨识系统产生错误,给基于语音辨识系统的应用带来安全风险。为了提升基于神经网络的自动语音辨识系统的安全性,需要对音频对抗样本的攻击和防御进行研究。基于此,分析总结对抗样本生成和防御技术的研究现状,介绍自动语音辨识系统对抗样本攻击和防御技术面临的挑战和解决思路。

关键词: 自动语音辨识, 深度学习, 对抗攻击, 对抗防御