Subtopic segmentation in the lecture speech

This paper proposes a method of segmentation that segments lecture videomaterial into subtopics based on speech signals for creation of educational video contents. To represent subtopics of video segments, the text recognized by automatic speech recognition (ASR) from a lecture speech was converted into an index using independent component analysis (ICA) instead of conventional TFIDF. This research attempted a method of segmentation using dynamic programming that minimizes the sum of cosine measures between adjacent indexes. The validity of the proposed method was evaluated using sample lecture videos. Results indicated that subtopic segmentation using automatic speech recognition performed as well as that using transcription text.