VoiceFormer: Fusing Non-Acoustic Motion Sensors for High-Fidelity Voice Synthesis in Mobile Devices
Bibliographic record
Abstract
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 article, 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".