Learning on demand - course lecture distillation by information extraction and semantic structuring for spoken documents

Authors:
Sheng-yi Kong;Miao-ru Wu;Che-kuang Lin;Yi-sheng Fu;Lin-shan Lee
Affiliations:
Speech Lab, College of EECS, National Taiwan University, Taipei, Taiwan, Republic of China;Speech Lab, College of EECS, National Taiwan University, Taipei, Taiwan, Republic of China;Speech Lab, College of EECS, National Taiwan University, Taipei, Taiwan, Republic of China;Speech Lab, College of EECS, National Taiwan University, Taipei, Taiwan, Republic of China;Speech Lab, College of EECS, National Taiwan University, Taipei, Taiwan, Republic of China
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Improved Semantic Retrieval of Spoken Content by Document/Query Expansion with Random Walk Over Acoustic Similarity Graphs

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)

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Abstract

This paper presents a new approach of organizing the course lectures (as spoken documents) for efficient learning on demand by the users. By the properly matching the course lectrues with the slides used, we divide the course lectures into hierarchical “major segments” with variable length based on the tiopics discussed. Key term extraction, hierarhical summarization and semantic structuring are then performed over these “major segments”. A key term graph is also constructed, based on which the various major segments of the course can be linked. In this way, the user can ask questions to the system, and develop his own road map of learning the knowledge he needs considring his available time and his background knowledge, based on the semantic structure provided by the system. A preliminary prototype system has been successfully developed with encouraging initial test results.