Question‐driven segmentation of lecture speech text: Towards intelligent e‐learning systems

Abstract Recently, lecture videos have been widely used in e‐learning systems. Envisioning intelligent e‐learning systems, this article addresses the challenge of information seeking in lecture videos by retrieving relevant video segments based on user queries, through dynamic segmentation of lecture speech text . In the proposed approach, shallow parsing such as part of‐speech tagging and noun phrase chunking are used to parse both questions and Automated Speech Recognition (ASR) transcripts. A sliding‐window algorithm is proposed to identify the start and ending boundaries of returned segments. Phonetic and partial matching is utilized to correct the errors from automated speech recognition and noun phrase chunking. Furthermore, extra knowledge such as lecture slides is used to facilitate the ASR transcript error correction. The approach also makes use of proximity to approximate the deep parsing and structure match between question and sentences in ASR transcripts. The experimental results showed that both phonetic and partial matching improved the segmentation performance, slides‐based ASR transcript correction improves information coverage, and proximity is also effective in improving the overall performance.