| 2 | RESEARCH ON THE MODEL OF SPEECH RECOGNITION AND UNDERSTANDING BY USING HIERARCHICAL INFORMATION FEEDBACK显示文摘In this paper according to the process of cognitive of human being to speech is put forward a model of speech recognition and understanding in a noisy environment. For speech recognition, two level modular Extended Associative Memory Neural Networks (EAMNN) are adopted. The learning speed is 9 times faster than that of the conventional BP net. It has high self-adaptability, robustness, fault toleration and associative memory ability to the noisy speech signals. To speech understanding, the structure of hierarchical analysis and examining faults which is a combination of statistic inference and syntactic rules is adopted, to pick up the candidates of the speech recognition and to predict the next word by the statistic inference base; and the syntactic rule base reduces effectively the recognition errors and candidates of acoustic level; then by comparing and rectifying errors through information feedback and guiding the succeeding speech process, the recognition of the sentence is realized. | Jiang Minghu Lin Biqin Yuan Baozong(institute of Information Science, Northern Jiaotong University, Beijing 100044) | 1999 | Journal of Electronics(China)1999，16，3： | 0 |
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