【摘要】 针对说话人识别中单一参数表征不够全面的特点,将抗噪性能一般的传统MFCC参数与鲁棒性更强的GFCC参数相互融合,并结合它们的动态特性构成一种新的混合参数.针对特征参数维数过高造成的冗余,研究了每种特征参数各分量与识别结果的关系,舍弃其中贡献较低的分量以实现特征参数降维的目的,并将混合参数应用于基于高斯混合模型的说话人识别系统.仿真实验表明,该混合特征参数具有更好的识别性能和抗噪性.更多还原

【Abstract】 Aiming at the issue that single feature parameter of speaker recognition has the shortcoming of low representation ability, a set of mixture feature parameters is formed by combining the single poor anti-noise Mel frequency cepstral coefficients(MFCC) with more robust Gammatone frequency cepstral coefficients(GFCC) and their dynamic differential in this paper. Since the high dimension of the mixture feature parameters, the relationships of each dimension of different feature parameters and recognition results is studied, where dimensionality reduction on high dimensional features is implemented by discarding the dimensions with low contribution ratio. After that, the combination of feature parameters was applied to the speaker recognition system based on Gaussian mixture model. Experimental results show that the combination of parameters can better describe the speakers’ feature and have better anti-noise capability.更多还原