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Automatic speech recognition systems have created exciting possibilities for ap- plications, however they also enable opportunities for systematic eavesdropping. We propose a method to camouflage a person’s voice over-the-air from these sys- tems without inconveniencing the conversation between people in the room. Stan- dard adversarial attacks are not effective in real-time streaming situations because the characteristics of the signal will have changed by the time the attack is exe- cuted. We introduce predictive attacks, which achieve real-time performance by forecasting the attack that will be the most effective in the future. Under real-time constraints, our method jams the established speech recognition system Deep- Speech 3.9x more than baselines as measured through word error rate, and 6.6x more as measured through character error rate. We furthermore demonstrate our approach is practically effective in realistic environments over physical distances.