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Proceedings Paper

Noise robust speech recognition based on wavelet-RBF neural network
Author(s): Xuemei Hou
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Paper Abstract

To solve the problem that recognition rates of speech recognition systems decrease in the noisy environment presently, a novel wavelet-RBF network model is presented in this paper. The model combines the time-frequency localization characteristic of wavelet and RBF neural network with the best classification capacity and ability of identification. A speech recognition system is mapped through wavelet-RBF network, which helps to overcome the defects of ANN such as the difficulty of rationally determining the network structure and the existence of partial optimal points. The experimental results show that the wavelet-RBF network is better than RBF network in SNRs and recognition rates.

Paper Details

Date Published: 11 July 2009
PDF: 5 pages
Proc. SPIE 7490, PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering, 74902O (11 July 2009); doi: 10.1117/12.836711
Show Author Affiliations
Xuemei Hou, Xi'an Institute of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 7490:
PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering
Honghua Tan; Qi Luo, Editor(s)

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