Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (16): 133-135.
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ZHANG Mei
Online:
Published:
张 梅
Abstract: This paper presents a method for speech endpoint detection based on wavelet analysis and fuzzy neural network to improve the adaptability and robustness of speech endpoint detection. Firstly, the characteristic quantities of speech signals are obtained by the wavelet transformation; then the input to fuzzy neural network can be computed based on these characteristic quantities, and finally the signal’s type can be determined. This paper mainly introduces how to obtain the characteristic quantities of signals and how to establish the model and the learning algorithm of fuzzy neural network. The experiments show that this method has better adaptability and robustness and can detect signals with different SNR, compared with the traditional detection methods.
Key words: speech endpoint detection, wavelet analysis, fuzzy neural network
摘要: 为了提高语音端点检测的适应性和鲁棒性,提出一种基于小波分析和模糊神经网络的语音端点检测方法。利用小波变换得到语音信号的特征量,以这些特征量为模糊神经网络的输入进行运算,判断出该信号的类别。介绍了信号特征量的提取以及模糊神经网络的模型、学习算法等。实验表明,与传统的检测方法相比,所提出的方法有较好的适应性和鲁棒性,对不同信噪比的信号都有较好的检测能力。
关键词: 语音端点检测, 小波分析, 模糊神经网络
ZHANG Mei. Method for speech endpoints detection based on fuzzy neural network[J]. Computer Engineering and Applications, 2012, 48(16): 133-135.
张 梅. 一种基于模糊神经网络的语音端点检测方法[J]. 计算机工程与应用, 2012, 48(16): 133-135.
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http://cea.ceaj.org/EN/Y2012/V48/I16/133