Research on the model of speech recognition and understanding by using hierarchical information feedback |
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Authors: | Minghu Jiang Biqin Lin Baozong Yuan |
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Affiliation: | (1) Institute of Information Science, Northern Jiaotong University, 100044 Beijing |
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Abstract: | 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. |
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Keywords: | Neural network Speech recognition and understanding Information feedback |
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