New algorithm for real time voice camouflage

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Automatic speech recognition systems pose risks for eavesdropping, prompting the development of a method to camouflage voices in real-time without disrupting conversations. Traditional adversarial attacks are ineffective in streaming scenarios due to signal changes, leading to the introduction of predictive attacks. This innovative approach forecasts the most effective future attack, significantly enhancing performance against systems like DeepSpeech, achieving a 3.9 times reduction in word error rate and 6.6 times in character error rate compared to baseline methods. The technique has been validated in realistic environments and over physical distances, showcasing its practical effectiveness. Audio samples and further resources are available online.
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An interesting approach to those eavesdropping devices in your life.
https://arxiv.org/pdf/2112.07076.pdf

ABSTRACT
Automatic speech recognition systems have created exciting possibilities for
applications, however they also enable opportunities for systematic eavesdropping.
We propose a method to camouflage a person’s voice over-the-air from these
systems without inconveniencing the conversation between people in the room.
standard 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
executed. 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.
 
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