独立分量分析在说话人识别技术中的应用

Application of independent component analysis to speaker recognition technique

独立分量分析方法能够将线性混合信号进行分离,得到统计独立的源信号,能用于提取组合语音的特征基函数。倒谱矢量符合ICA变换的假设条件,用ICA方法对MFCC特征进行转换得到ICA特征基,继而用于说话人识别,建立了一个基于独立分量分析的说话人识别系统。实验结果表明,在噪声环境下此系统具有更高的识别率。

Independent Component Analysis（ICA） is a new method to separate the observed mixtures so as to estimate the independent components and can be used to extract the feature bases functions.The characteristic of the cepstrum vector agrees with the assumption of the ICA transformation.ICA bases is transformed from improved robust Mel Frequency Cepstrum Coefficients（MFCC） by ICA and then used in speaker recognition.A novel speaker recognition system based on ICA is presented.The results of experiments show that the proposed system has a higher recognition rate in noisy environments.