A New Multimedia Content Skimming Method based on Speech Emphasis Extraction and Its Application to Content Variations

摘要

We propose Choco-Para, a multimedia content skimming technique; its application to a variety of content types is described. Based on automatic speech emphasis extraction, Choco-Para extracts speech attributes, prosodic parameters such as pitch, power, and speaking rate, and uses the data to estimate the degree of emphasis of each spoken phrase. By computing the degree of the emphasis curve, Choco-Para can generate a skimmed edition at an arbitrary skimming rate by selecting emphasized speech portions via dynamic threshold logic. Choco-Para uses three types of prosodic parameters and both short term and long term deviation. Experiments assess the contributions of each prosodic parameter and deviation type. They show that estimation accuracy is optimized by using both short and long term deviation with regard to pitch, power, and speaking rate. The results confirm that Choco-Para supports a wide variety of multimedia content.