Abstract
While speaking, humans exhibit a number of recognizable patterns; most notably, the repetitive nature of mouth movement from closed to open. The following paper presents a novel method to computationally determine when video data contains a person speaking through the recognition and tally of lip facial closures within a given interval. A combination of Haar-Feature detection and eigenvectors are used to recognize when a target individual is present, but by detecting and quantifying spasmodic lip movements and comparing them to the ranges seen in true positives, we are able to predict when true speech occurs without the need for complex facial mappings. Although the results are within a reasonable accuracy range when compared to current methods, the comprehensibility and simple nature of the approach used can reduce the strenuousness of current techniques and, if paired with synchronous audio recognition methods, can streamline the future of voice activity detection as a whole.
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