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Proceedings Paper

Speech recognition method based on genetic vector quantization and BP neural network
Author(s): Li'ai Gao; Lihua Li; Jian Zhou; Qiuxia Zhao
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Paper Abstract

Vector Quantization is one of popular codebook design methods for speech recognition at present. In the process of codebook design, traditional LBG algorithm owns the advantage of fast convergence, but it is easy to get the local optimal result and be influenced by initial codebook. According to the understanding that Genetic Algorithm has the capability of getting the global optimal result, this paper proposes a hybrid clustering method GA-L based on Genetic Algorithm and LBG algorithm to improve the codebook.. Then using genetic neural networks for speech recognition. consequently search a global optimization codebook of the training vector space. The experiments show that neural network identification method based on genetic algorithm can extricate from its local maximum value and the initial restrictions, it can show superior to the standard genetic algorithm and BP neural network algorithm from various sources, and the genetic BP neural networks has a higher recognition rate and the unique application advantages than the general BP neural network in the same GA-VQ codebook, it can achieve a win-win situation in the time and efficiency.

Paper Details

Date Published: 10 July 2009
PDF: 4 pages
Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 748915 (10 July 2009); doi: 10.1117/12.836816
Show Author Affiliations
Li'ai Gao, Agricultural Univ. of Hebei (China)
Lihua Li, Agricultural Univ. of Hebei (China)
Jian Zhou, Qinhuangdao Asano Cement Co., Ltd. (China)
Qiuxia Zhao, Agricultural Univ. of Hebei (China)

Published in SPIE Proceedings Vol. 7489:
PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering
Honghua Tan; Qi Luo, Editor(s)

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