Gyrophone: Recognizing Speech from Gyroscope Signals
By Yan Michalevsky, Dan Boneh, Gabi Nakibly. 2014
343 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Changchang Liu
- Supriyo Chakraborty
- Prateek Mittal
- Timothy Trippel
- Guoming Zhang
- Chen Yan
- Ofir Weisse
- Andrea Capponi
Citations in the corpus
- SEANet: A Multi-modal Speech Enhancement Network (2020, neutral): “For example, gyroscope signals were used to recognize speech in [22], while [23] reconstructs speech from accelerometer-sensed re-verberations induced by smartphone loudspeakers.”
- DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, methodology): “The commonly used features for speech-to-text translation are Melfrequency Cepstral Coefficients (MFCC), spectrogram and Ngram [48], [61], [93], while the popular features for speaker identity recognition are MFCC, spectrogram and voice biometrics of the user (such as her pitch, accent, etc.”
- A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “Other examples consist of distinguishing transportation modes [74]–[77], recognition of speech [78] and driving style [79] and for indoor navigation [80]–[82].”
- : Privacy on Mobile Devices – It ’ s Complicated (2016, background): “Microphone [113][115] [62] ? [56] [104][105] [10, 106][107] [10, 106][107]”
- SoK: Privacy on Mobile Devices – It’s Complicated (2016, background): “Microphone [113][115] [62] ? [56] [104][105] [10, 106][107] [10, 106][107]”
- Mechanoacoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch (2019, unknown): “…capabilities in this context, with demonstrated examples in capturing signatures of cardiac mechanics such as seismocardiograms or ballistocardiograms 11–14 , respiratory rate and sounds 15–21 , sounds of swallowing 22–25 , vocals 26,27 , changes in body position and motion 28–30 and others 31 .”
- DolphinAttack: Inaudible Voice Commands (2017, methodology): “[36] utilized MEMS gyroscopes to measure acoustic signals, which reveal the speaker information.”
- Rocking Drones with Intentional Sound Noise on Gyroscopic Sensors (2015, background): “the capability of the gyroscopes of smartphones to measure acoustic vibrations at a low frequency band, a new attack was proposed to eavesdrop speech [59].”
- Spearphone: A Speech Privacy Exploit via Accelerometer-Sensed Reverberations from Smartphone Loudspeakers (2019, methodology): “Since the speech signals that produce vibrations consist of low frequencies, our threat model is much weaker than the one used in [6].”
- WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks (2017, methodology): “Michalevsky et al. show that gyroscopes in smart-phones can be used as a micro-phone to eavesdrop on conversations [16].”