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。
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.
出处
《计算机工程与应用》
CSCD
2012年第16期133-135,167,共4页
Computer Engineering and Applications
基金
安徽省自然科学基金(No.KJ2011Z092)
关键词
语音端点检测
小波分析
模糊神经网络
speech endpoint detection
wavelet analysis
fuzzy neural network
