Speaker recognition using frequency filtered spectral energies

The spectral parameters that result from filtering the frequency sequence of log mel-scaled filter-bank energies with a simple first or second order FIR filter have proved to be an efficient speech representation in terms of both speech recognition rate and computational load. Recently, the authors have shown that this frequency filtering can approximately equalize the cepstrum variance enhancing the oscillations of the spectral envelope curve that are most effective for discrimination between speakers. Even better speaker identification results than using melcepstrum have been obtained on the TIMIT database, especially when white noise was added. On the other hand, the hybridization of both linear prediction and filter-bank spectral analysis using either cepstral transformation or the alternative frequency filtering has been explored for speaker verification. The combination of hybrid spectral analysis and frequency filtering, that had shown to be able to outperform the conventional techniques in clean and noisy word recognition, has yield good text-dependent speaker verification results on the new speaker-oriented telephone-line POLYCOST database.Peer ReviewedPostprint (published version

DolphinAtack: Inaudible Voice Commands

Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems(VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though hidden, are nonetheless audible. In this work, we design a completely inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validate DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions. We validate that it is feasible to detect DolphinAttack by classifying the audios using supported vector machine (SVM), and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.Comment: 15 pages, 17 figure