Speaker identification system using empirical mode decomposition and an artificial neural network
Speaker identification system using empirical mode decomposition and an artificial neural network
复制标题
DOI：
10.1016/j.eswa.2010.11.013
复制
发表时间：
2011-05
期刊：
影响因子：
--
通讯作者：
Jian-da Wu;Yi-Jang Tsai
中科院分区：
文献类型：
--
作者：
Jian-da Wu;Yi-Jang Tsai
提出了一种将经验模式分解（EMD）特征提取方法和人工神经网络相结合的说话人识别系统。EMD是一种自适应多分辨率分解技术，似乎适合于非线性，非平稳数据分析。经验模态分解（EMD）对时间序列的复信号进行筛选，在保持其原有特性的前提下，得到有用的本征模态函数（IMF）分量。计算各分量的能量可以降低计算维数，提高分类性能。这些特征被用作神经网络分类器的输入，用于说话人识别。在说话人识别中，分别采用反向传播神经网络（BPNN）和广义回归神经网络（GRNN）对系统的性能和训练时间进行了验证。实验结果表明，与BPNN相比，GRNN在使用EMD方法进行特征提取时可以获得更好的识别率性能。
This paper presents a speaker identification system using empirical mode decomposition (EMD) feature extraction method and artificial neural network in speaker identification. The EMD is an adaptive multi-resolution decomposition technique that appears to be suitable for non-linear, non-stationary data analysis. The EMD sifts the complex signal of time series without losing its original properties and then obtains some useful intrinsic mode function (IMF) components. Calculating the energy of each component can reduce the computation dimensions and enhance the performance of classification. The features were used as inputs to neural network classifiers for speaker identification. In the speaker identification, the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) were applied to verify the performances and the training time in the proposed system. The experimental results indicated the GRNN can achieve better recognition rate performance with feature extraction using the EMD method than BPNN.