Research on Text-Independent Speaker Recognition 

Methods Using Wavelet Neural Network

Bai Ying; Zhao Zhen-dong; Qi Yin-cheng; Wang Bin; Guo Jian-yong

Volume 28 Issue 6

Jun. 2006

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Article Navigation > Journal of Electronics & Information Technology > 2006 > 28(6): 1036-1039

Bai Ying, Zhao Zhen-dong, Qi Yin-cheng, Wang Bin, Guo Jian-yong. Research on Text-Independent Speaker Recognition Methods Using Wavelet Neural Network[J]. Journal of Electronics & Information Technology, 2006, 28(6): 1036-1039.

Citation:

Bai Ying, Zhao Zhen-dong, Qi Yin-cheng, Wang Bin, Guo Jian-yong. Research on Text-Independent Speaker Recognition Methods Using Wavelet Neural Network[J]. Journal of Electronics & Information Technology, 2006, 28(6): 1036-1039.

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Research on Text-Independent Speaker Recognition Methods Using Wavelet Neural Network

Received Date: 2004-11-01
Rev Recd Date: 2005-03-22
Publish Date: 2006-06-19

Abstract

Abstract

The approach for speaker recognition based on neural networks is able to emulate the function of human brain in some degree, so it is a main implementation technology in the speaker recognition. But it is difficult to determine the number of hidden layer neurons, slowly convergent and easy to fall into local minimum point. The model of wavelet neural networks is studied. The structure of the network and learning algorithm are given. The recognition correctness reaches to 99.5% for 5 speakers using Mel frequency cepstral coefficient as feature parameters. The experimental at results show that the learning rate and recognition correctness are improved much compared to the BP networks. It has a good application prospect and worth to research further more.

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References(1)

References

Zhang Qinhua, Benveniste Al. Wavelet networks[J].IEEE Trans. on Neural Networks.1992, 3(6):889-[2]Szu H, Telfer B, Kadambe S. Neural network adaptive wavelets for signal representation and classification. Optical Engineering, 1992, 31(9)：907.1016.[3]彭玉华. 小波变换与工程应用. 北京: 科学出版社, 2002：7.8[4]Zhang J, Walter G. Wavelet neural networks for function learning[J].IEEE Trans. on Signal Processing.1995, 43(6):1485-[5]李卫斌, 刘芳.小波神经网络的构造. 模式识别与人工智能，2003, 16(4)：403.406.[6]焦李成. 神经网络的应用与实现. 西安：西安电子科技大学出版社, 1996, 第一章.[7]Yoshihiro Yamamoto, Nikiforuk P N. A new supervised learning algorithm for multilayered and inter-connected neural networks[J].IEEE Trans. on Neural Network.2000,11(1):36-[8]李金平，王风涛，杨波. BP小波神经网络快速学习算法研究. 系统工程与电子技术，2001, 23(8)：72.75.[9]赵学智，邹春华，陈统坚. 小波神经网络的参数初始化研究. 华南理工大学学报(自然科学版), 2003, 31 (2)：77.80.[10]Lamel L F, Kessel R H, Seneff S. Speech database development :Design and analysis of the acoustic-phonetic corpus. Proc.Speech Recognition Workshop(DARPA), 1986: 100.109.[11]甄斌，吴玺宏，刘志敏. 语音识别和说话人识别中各倒谱分量的相对重要性. 北京大学学报, 2001, 37(3): 371.378.

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