2001 Fiscal Year Final Research Report Summary
Study on Multimedia Teaching Material by Hyper-Media and Content Analysis of Lecture Videos
| Project/Area Number |
11480081
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Ryukoku University |
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Principal Investigator |
YASUO Ariki Ryukoku University, Electronics and Informatics PROFESSOR, 理工学部・電子情報学科, 教授 (10135519)
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| Co-Investigator(Kenkyū-buntansha) |
KUMANO Masahito Ryukoku University, Electronics and Informatics Assistant, 理工学部・電子情報学科, 実験助手 (50319498)
KAWAKAMI Hajimu Ryukoku University, Electronics and Informatics Lecturere, 理工学部・電子情報学科, 講師 (60298734)
KOBUCHI Yoichi Ryukoku University, Electronics and Informatics PROFESSOR, 理工学部・電子情報学科, 教授 (60025450)
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| Project Period (FY) |
1999 – 2001
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| Keywords | Digital archive / Hyperlink / Speech dictation / Video summarization / Confidence / Content description / Similar Video / Telop |
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| Research Abstract |
Main results of this study from 1999 to 2001 are summarized into seven points as follows;
1. Indexing to spoken documents based on high speed and high accurate speech recognition: Speech decoding based on phonemes or words error minimization was proposed as well as iterative adaptation of acoustic models in unsupervised mode.
2. Indexing to noisy speech based on noise and BGM robust speech recognition: Speech recognition based on non-stationary as well as stationary noise reduction was proposed by using Kalman filter and MLLR.
3. Speaker indexing based on speaker recognition: Speaker recognition based on phoneme and speaker separation was proposed and individual person was indexed based on the proposed method.
4. Indexing to video image based on character and image recognition: Video caption or flip recognition was proposed by carrying out the video caption frame selection, effective binarization and OCR.
5. Topic segmentation based on speech and character recognition: News videos were segmented into individual topic based on word space method proposed in this study. In commercial video, the topic segmentation was proposed by integrating video caption recognition and speech recognition.
6. Structuring and content description to video image: Table of contents of video images was produced after indexing and topic segmentation.
7. Topic retrieval and summarization for hyperlink construction: Cross media retrieval was proposed as well as the video clip extraction where the specific person was speaking about the specific topics.
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