【摘要】 提出主元分析 PCA(Principal Component Analysis)用于语音检测的方法研究.用主元分析法在多维空间中建立坐标轴,将待处理信号投影到该坐标轴中,通过分析投影结果判断是否为语音信号.通过将语音和非语音分别建立子空间,来区分语音和非语音信号.该方法不同于常规的语音时域、频域处理方法,而是在多维空间中对信号进行分析.实验结果表明,该方法准确率高、简单、容易实现,而且能区分多种非语音信号.更多还原

【Abstract】 It is essential for speech processing system to have robust speech detection.In this paper,a PCA(principal component analysis)-based speech detection method is proposed.A good result of the examination by using this method is gotten.In this method,speech and non-speech subspaces are created respectively by using PCA.The result of fast PCA is the basis of the new subspace. By analysis the distribution of the data in subspace,the speech and non-speech can be detected respectively.Creating a number of different type non-speech subspaces can get a better performance than creating one.更多还原