Low frequency ultrasonic voice activity detection using convolutional neural networks

Low frequency ultrasonic mouth state detection uses reflected audio chirps from the face in the region of the mouth to determine lip state, whether open, closed or partially open. The chirps are located in a frequency range just above the threshold of human hearing and are thus both inaudible as well as unaffected by interfering speech, yet can be produced and sensed using inexpensive equipment. To determine mouth open or closed state, and hence form a measure of voice activity detection, this recently invented technique relies upon the difference in the reflected chirp caused by resonances introduced by the open or partially open mouth cavity. Voice activity is then inferred from lip state through patterns of mouth movement, in a similar way to video-based lip-reading technologies. This paper introduces a new metric based on spectrogram features extracted from the reflected chirp, with a convolutional neural network classification back-end, that yields excellent performance without needing the periodic resetting of the template closed-mouth reflection required by the original technique.