The audio adversarial example has been demonstrated to be an effective attack which leads to prediction errors of the intelligent voice control system (e.g., deep neural network based speech recognition service), despite resembling a valid input to our human beings. An ideal adversarial example attack should have four major advantages, including 1) utilizing a universal adversarial perturbation against arbitrary voice commands, 2) tricking a model to get an incorrect and targeted result, 3) imperceptible to users even in a silent place and 4) validating in an over-the-air (OTA) scenario as well. However, existing studies mainly involve several but not all of these criteria. In this paper, we propose UTIO, a universal, targeted, imperceptible and OTA audio adversarial example design, which leverages one perturbation to fool a speech recognition model in OTA scenarios. Moreover, a variety of speeches can be misled to a targeted threat command imperceptibly. To harvest such benefits, we leverage two targeted loss functions to generate adversarial perturbations, and employ the psychoacoustic principle to further conceal the attack. Finally, we actively embed additional distortions, occurred during the physical propagation, in the process of perturbation generation to make UTIO still valid in an OTA scenario. Extensive experiments show that UTIO can perform 94.15% success attack rate locally, i.e., without physical propagation, while retaining 93.44% attack rate in an OTA scenario. In addition, three types of defensive strategies are also introduced to resist against our attack. © 2023 IEEE.