  Voice Activity Detection (VAD) refers to the task of identification of
regions of human speech in digital signals such as audio and video. While VAD
is a necessary first step in many speech processing systems, it poses
challenges when there are high levels of ambient noise during the audio
recording. To improve the performance of VAD in such conditions, several
methods utilizing the visual information extracted from the region surrounding
the mouth/lip region of the speakers' video recording have been proposed. Even
though these provide advantages over audio-only methods, they depend on
faithful extraction of lip/mouth regions. Motivated by these, a new paradigm
for VAD based on the fact that respiration forms the primary source of energy
for speech production is proposed. Specifically, an audio-independent VAD
technique using the respiration pattern extracted from the speakers' video is
developed. The Respiration Pattern is first extracted from the video focusing
on the abdominal-thoracic region of a speaker using an optical flow based
method. Subsequently, voice activity is detected from the respiration pattern
signal using neural sequence-to-sequence prediction models. The efficacy of the
proposed method is demonstrated through experiments on a challenging dataset
recorded in real acoustic environments and compared with four previous methods
based on audio and visual cues.
