Inductive inference of recognition rules for continuous speech recognition based on knowledge base

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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.