MicPro: Microphone-based Voice Privacy Protection

Hundreds of hours of audios are recorded and transmitted over the Internet for voice interactions such as virtual calls or speech recognitions. As these recordings are uploaded, embedded biometric information, i.e., voiceprints, is unnecessarily exposed. This paper proposes the first privacy-enhanced microphone module (i.e., MicPro) that can produce anonymous audio recordings with biometric information suppressed while preserving speech quality for human perception or linguistic content for speech recognition. Limited by the hardware capabilities of microphone modules, previous works that modify recording at the software level are inapplicable. To achieve anonymity in this scenario, MicPro transforms formants, which are distinct for each person due to the unique physiological structure of the vocal organs, and formant transformations are done by modifying the linear spectrum frequencies (LSFs) provided by a popular codec (i.e., CELP) in low-latency communications. To strike a balance between anonymity and usability, we use a multi-objective genetic algorithm (NSGA-II) to optimize the transformation coefficients. We implement MicPro on an off-the-shelf microphone module and evaluate the performance of MicPro on several ASV systems, ASR systems, corpora, and in real-world setup. Our experiments show that for the state-of-the-art ASV systems, MicPro outperforms existing software-based strategies that utilize signal processing (SP) techniques, achieving an EER that is 5 similar to 10% higher and MMR that is 20% higher than existing works while maintaining a comparable level of usability.