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Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

机译:改进的神经模糊网络和改进的遗传算法在语音识别中的应用

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This paper presents the recognition of speech commands using a modified neural fuzzy network (NFN). By introducing associative memory (the tuner NFN) into the classification process (the classifier NFN), the network parameters could be made adaptive to changing input data. Then, the search space of the classification network could be enlarged by a single network. To train the parameters of the modified NFN, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an eBook experimentally to illustrate the design and its merits.
机译:本文介绍了使用改进的神经模糊网络(NFN)识别语音命令。通过将关联存储器(调谐器NFN)引入分类过程(分类器NFN),可以使网络参数适应于更改输入数据。然后,可以通过单个网络来扩大分类网络的搜索空间。为了训练改进型NFN的参数,提出了一种改进的遗传算法。作为一个应用示例,提出的语音识别方法在eBook中实验性地实现,以说明其设计和优点。

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