计算机科学 ›› 2012, Vol. 39 ›› Issue (6): 244-246.

• 人工智能 • 上一篇    下一篇

小波分析和支持向量机相融合的语音端点检测算法

朱恒军,于泓博,王发智   

  1. (齐齐哈尔大学通信与电子工程学院 齐齐哈尔161006)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Speech Endpoints Detection Algorithm Based on Support Vector Machine and Wavelet Analysis

  • Online:2018-11-16 Published:2018-11-16

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支持向量机的语音端点检测算法。首 先利用小波变换提取语音信号的特征量,然后将这些特征量作为支持向量机的输入进行训练和建模,最后判断出该信 号的类别。仿真实验表明,相对于传统的语音端点检测算法,小波分析和支持向量机的检测算法提高了语音端点检测 的正确率,有效降低了虚检率和漏检率,具有更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: 小波分析,支持向量机,语音端点,特征提取

Abstract: In order to improve the adaptability and robustness of speech endpoint detection, this paper presented a algo- rithm for speech endpoint detection based on wavelet analysis and support vector machine. Firstly, the characteristic quantities of speech signals are obtained by the wavelet transformation. Then the input to support vector machine can be computed based on these characteristic quantities. Finally the signal's type can be determined. The simulation experi- menu results show that the proposed algorithm improves the detection rate, has better adaptability and robustness, and can detect signals with different SNR, compared with the traditional detection algorithms.

Key words: Wavelet analysis, Support vector machine, Speech Endpoints, Feature extraction

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