VoiceFormer: Fusing Non-Acoustic Motion Sensors for High-Fidelity Voice Synthesis in Mobile Devices

With the popularity of mobile devices, a variety of motion sensors are integrated to enhance the user experience. Although existing studies demonstrated that non-acoustic motion sensors can be attacked by adversaries, they overlook the limited sampling frequencies of motion sensors (e.g. < 500 Hz) in mobile devices and are evaluated in the controlled laboratory settings. In this paper, we explore a new attack model on non-acoustic motion sensors based on the off-the-shelf mobile devices. We propose a general framework named VoiceFormer to synthesize high-fidelity speeches based on the vibrations of accelerometers and gyroscopes with a low sampling frequency. Specifically, in VoiceFormer , we introduce a signal alignment approach to remove the time offsets between two nonsynchronous signals, and leverage Time Interleaved Analog-Digital-Conversion (TI-ADC) to generate a high-frequency synthetic signal (e.g. > 8 KHz) based on the vibration signals of accelerometers and gyroscopes on the same motherboard. To synthesize the high-fidelity acoustic waveforms, we propose a wavelet-based generative adversarial network to learn the spatiotemporal latent mapping between vibrations and original speech signals. Extensive experimental results demonstrate the feasibility of voice synthesis by spying the low-frequency non-acoustic motion sensors in off-the-shelf mobile devices. VoiceFormer shows impressive performance in the synthesized acoustical signals with a Mean Opinion Score of 3.38. Although there are significant differences of mobile devices in hardware settings, VoiceFormer shows robust performance in synthesizing intelligible voice signals. Our results suggest that eavesdropping an off-the-shelf mobile device remotely by fusing non-acoustic sensors is feasible.