Detecting discussion scenes in instructional videos

This paper addresses the problem of detecting discussion scenes in instructional videos using statistical approaches. Specifically, given a series of speech segments separated from the audio tracks of educational videos, we first model the instructor using a Gaussian mixture model (GMM), then a four-state transition machine is designed to extract discussion scenes in real-time, based on detected instructor-student speaker change points. Meanwhile, we keep updating the GMM model to accommodate the instructor´s voice variation along time. Promising experimental results have been achieved on five educational (IBM MicroMBA program) videos, and very interesting instruction/teaching patterns have been observed. The extracted scene information would facilitate the semantic indexing and structuralization of instructional video content