陈雪芳. 小波分析和粒子群优化神经网络的语音端点检测[J]. 微电子学与计算机, 2013, 30(9): 94-97.
引用本文: 陈雪芳. 小波分析和粒子群优化神经网络的语音端点检测[J]. 微电子学与计算机, 2013, 30(9): 94-97.
CHEN Xue-fang. Wavelet Analysis and the Neural Network of Particle Swarm Optimization for Speech Endpoints Detection[J]. Microelectronics & Computer, 2013, 30(9): 94-97.
Citation: CHEN Xue-fang. Wavelet Analysis and the Neural Network of Particle Swarm Optimization for Speech Endpoints Detection[J]. Microelectronics & Computer, 2013, 30(9): 94-97.

小波分析和粒子群优化神经网络的语音端点检测

Wavelet Analysis and the Neural Network of Particle Swarm Optimization for Speech Endpoints Detection

  • 摘要: 为了提高语音端点检测的适应性和鲁棒性,提出一种小波分析和粒子群优化神经网络(WA -PSO -BP)的语音端点检测算法。首先利用小波分析提取语音信号的特征量,然后将特征量作为输入BP神经网络进行学习,并采用粒子群算法优化BP神经网络参数,从而建立语音端检测模型。仿真结果表明,WA -PSO -BP提高了语音端点检测正确率,有效降低了虚检率和漏检率。这说明WA -PSO -BP是一种可行性较高,环境适应性较强的语音检测算法。

     

    Abstract: In order to improve the adaptability and robustness of speech endpoint detection,this paper proposes a speech endpoint detection method base on WA-PSO-BP. Firstly, the feature of speech signals are extracted by wavelet analysis;then the features are input to BP neural network to build the speech endpoints detection model in which the BP neural network's parameters are optimized by particle swarm optimization algorithm.The experiments results show that the proposed method has improved the detection rate, and it has better adaptability and robustness.WA-PSO-BP has high reliability makes it suitable for application in different kinds of environments.

     

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