Noisy Speech Recognition Based On RBF Neural Network

被引：0

作者：

Yan Gang ^{[1
]}

Kong Haidong ^{[1
]}

Yu Yang ^{[1
]}

Zheng Xiaoxia ^{[1
]}

机构：

[1] Hebei Univ Technol, Sch Comp Sci & Software, Tianjin, Peoples R China

来源：

ADVANCED MATERIALS AND INFORMATION TECHNOLOGY PROCESSING, PTS 1-3 | 2011年 / 271-273卷

关键词：

Pattern recognition; noisy speech; RBF neural network; FPE standard;

D O I：

10.4028/www.scientific.net/AMR.271-273.597

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

A noisy speech recognition method based on improved RBF neural network is presented, which the parameters of hidden layer are trained dynamically, and Akaike's final prediction error standard (FPE) is employed to simplify the network. Comparing with two other training methods of RBF network, experimental results based on noisy speech samples show that this method achieves excellent performance in terms of recognition rate and recognition speed.

引用

页码：597 / 602

页数：6

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