  Voice Recognition Systems (VRSs) employ deep learning for speech recognition
and speaker recognition. They have been widely deployed in various real-world
applications, from intelligent voice assistance to telephony surveillance and
biometric authentication. However, prior research has revealed the
vulnerability of VRSs to backdoor attacks, which pose a significant threat to
the security and privacy of VRSs. Unfortunately, existing literature lacks a
thorough review on this topic. This paper fills this research gap by conducting
a comprehensive survey on backdoor attacks against VRSs. We first present an
overview of VRSs and backdoor attacks, elucidating their basic knowledge. Then
we propose a set of evaluation criteria to assess the performance of backdoor
attack methods. Next, we present a comprehensive taxonomy of backdoor attacks
against VRSs from different perspectives and analyze the characteristic of
different categories. After that, we comprehensively review existing attack
methods and analyze their pros and cons based on the proposed criteria.
Furthermore, we review classic backdoor defense methods and generic audio
defense techniques. Then we discuss the feasibility of deploying them on VRSs.
Finally, we figure out several open issues and further suggest future research
directions to motivate the research of VRSs security.
