2004 Fiscal Year Final Research Report Summary
Development of a supporting system for creation of educational video contents using robust automatic speech recognition technology
| Project/Area Number |
14580246
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Educational technology
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| Research Institution | Ishikawa National College of Technology |
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Principal Investigator |
KANEDERA Noboru Ishikawa National College of Technology, Department of Electronics and Information Engineering, Associate Professor, 電子情報工学科, 助教授 (50194931)
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| Project Period (FY) |
2002 – 2004
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| Keywords | Educational video contents / Video segmentation / Independent component analysis / Automatic speech recognition / Dynamic Programming / Video retrieval / Video edit system |
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| Research Abstract |
We developed a supporting system for creation of educational video contents. The system automatically segments a lecture video material into subtopics based on speech signals. To represent subtopics of video scenes, 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 TF-IDF. This research attempted a method of segmentation using dynamic programming that minimizes the sum of cosine distances between adjacent indexes that represent subtopics of video scenes. The validity of the proposed method was evaluated using sample lecture videos uttered by five lecturers. Results indicated that scene segmentation using automatic speech recognition performed as well as that using transcription text.
Editing a video requires searching for subtopic segmentation positions, and extraction of necessary video segments, or removing unnecessary video segments. In particular, when searching subtopic segmentation positions, a large amount of time and efforts are required to review the video from beginning to end. That is, it is hard work to search subtopic segmentation positions. It is therefore expected to reduce the editing time and efforts by the developed system with automatic subtopic segmentation. In this research, we carried out subjective evaluation by 16 examinees and 5 lecture video materials to confirm the effect of automatic subtopic segmentation. As a result, 75% of examinees answered that the editing method with automatic subtopic segmentation is better than that without segmentation. Moreover, the average editing time was reduced by about 14%.
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