| Project/Area Number |
63580027
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Informatics
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| Research Institution | Osaka University |
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Principal Investigator |
MIZOGUCHI Riichiro Osaka Univ., The Institute of Scientific and Industrial Research, Professor, 産業科学研究所, 助教授 (20116106)
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| Project Period (FY) |
1988 – 1989
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| Project Status |
Completed (Fiscal Year 1989)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 1989: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1988: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | Knowledge base / Speech recognition / Inductive learning / Knowledge base building / Expert system / 知識ベース / 帰納推論 / 学習 / 知識ベース構築 / エキスパートシステム |
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| Research Abstract |
I have been engaged in developing an environment for knowledge based system construction for continuous speech recognition expert SPREX. The final goal of this research is to establish a methodology of knowledge engineering approach to speech recognition. Specifically, I developed a new environment for knowledge base building which integrates both a learning mechanism and hand-made rule construction. The result is summarized as follows:
1. Generation of new attributes: A method for generating some new attributes by combining existing attributes is designed.
2. Generalization of decision tree: Two kinds of operators for generalization of decision tree are devised so as to augment the classification performance of the tree for unknown samples.
3. Evaluation of the method: An experiment was done to evaluate the performance of the learning method. The recognition result of the decision tree obtained according to the learning method was 82% and that of man-made rule was 88%, which demonstrates the high performance of the learning method considering that it has no a priori knowledge about the domain.
4. We developed a final system which has a new architecture integrating both learning and hand-crafting rule construction method. It attained 88% of recognition rate for the same data above, which is comparable to that of man-made rules. Thus, we obtained a new learning system of very high performance.
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