Recurrent Fuzzy Neural Networks for Speech Detection

被引：0

作者：

Wu, Gin-Der ^{[1
]}

Zhu, Zhen-Wei ^{[1
]}

机构：

[1] Natl Chi Nan Univ, Dept Elect Engn, Puli, Taiwan

来源：

2015 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY) | 2015年

关键词：

refurrent fuzzy neural networks; gradient descent method; speech detection; WORD BOUNDARY DETECTION; INFERENCE NETWORK; SYSTEMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a recurrent fuzzy neural network (RFNN) for speech detection. The underlying notion of the proposed RFNN is to consider minimum classification error (NICE) and minimum training error (MTE). The weights of RFNN are updated by maximizing the discrimination among different classes in NICE. Besides, the parameter learning adopts the gradient descent method to reduce the cost function in MTE. Therefore, the novelty of this paper is to minimize the cost function and maximize the discriminative capability. Finally, the experiment of speech detection is applied to test the proposed RFNN, the results show that the proposed RFNN exhibits excellent classification performance.

引用

页码：18 / 21

页数：4

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