【摘要】 提出了两种方法以克服背景噪音的干扰并提高说话人识别系统的鲁棒性：一种方法是基于频谱峰值位置受背景噪音影响相对较小的考虑,将子带幅度信息和子带Mel频谱质心(SMSC)相结合；另一种方法是通过计算类转移概率矩阵来对隐藏于高斯混合相关(GMC)中的说话人高层信息进行建模。实验表明SMSC和GMC都能够在平稳噪音环境下提高说话人识别系统的鲁棒性,并且采用SMSC和GMC的GMM-UBM系统跟使用传统MFCC的GMM-UBM基准系统相比,平均错误率下降了11．7%。更多还原

【Abstract】 In order to overcome the influence of background noises and improve the robustness of speaker identification systems,two methods were proposed:One is to incorporate subband amplitude information with subband Mel-spectrum centroid(SMSC)because spectral peak positions remain practically unaffected in presence of additive noise.The other is to use a class transition probability matrix to model the high-level information hidden in Gaussian mixture correlation (GMC).Experiments showed that SMSC and GMC could improve the robustness of a speaker identification system in stationary noises,respectively.The average error rate of GMM-UBM system using SMSC and GMC can be reduced by 11.7% compared to conventional GMM-UBM system using MFCC.更多还原