| 基于小波神经网络的语音端点检测算法 |
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| 引用本文： | 胡 伟,郑明才.基于小波神经网络的语音端点检测算法[J].计算机工程与应用,2013,49(12):191-194. |
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| 作者姓名： | 胡 伟 郑明才 |
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| 作者单位： | 湖南第一师范学院 科研处，长沙 410002 |
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| 摘 要： | 为了提高语音端点检测效果，将小波分析和神经网络相融合，提出一种基于小波神经网络的语音端点检测算法（WA-PCA-RBF）。利用小波分析提取语音信号的特征向量，采用主成分分析法选择语音信号特征，消除冗余特征，将选择特征向量作为RBF神经网络输入，通过遗传算法优化RBF神经网络参数建立语音端检测模型。结果表明，相对于传统语音端点检测算法，WA-PCA-RBF提高了语音端点检测正确率，具有更好的适应性和鲁棒性，可满足实际系统需求。
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| 关 键 词： | 小波分析 神经网络 语音端点 特征提取 特征选择 |
Speech endpoints detection method based on wavelet neural network |
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| HU Wei,ZHENG Mingcai.Speech endpoints detection method based on wavelet neural network[J].Computer Engineering and Applications,2013,49(12):191-194. |
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| Authors: | HU Wei ZHENG Mingcai |
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| Affiliation: | Department of Science and Research, Hunan First Normal University, Changsha 410002, China |
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| Abstract: | In order to improve the adaptability and robustness of speech endpoint detection, this paper proposes a speech endpoint detection method based on wavelet analysis and neural network（WA-PCA-RBF）. The features of speech signals are extracted by wavelet analysis; the features are selected by Principal Component Analysis to remove redundant features; the selected features are input to RBF neural network to build the speech endpoints detection model in which the RBF neural network’s parameters are optimized by genetic algorithm. The results show that the proposed method has improved the detection rate, and it has better adaptability and robustness in complicated noise environment compared with the traditional detection methods. |
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| Keywords: | wavelet analysis neural network speech endpoints feature extraction feature selection |
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