一种基于非均匀谱系数和GMM的语音质量评估方法

本文提出了一种新的基于GMM和非均匀线性预测倒谱系数(NLPC)的客观语,音质量评估方法.首先,通过Bark双线性变换(BBT)对线性频谱进行频谱弯折,弯折后的频谱符合人耳听觉感知的非均匀特性.然后通过对非均匀谱的线性预测计算出NLPC.提取参考语音的NLPC用来对高斯混合模型进行训练.通过训练对参考语音建立参考模型.由参考模型和失真语音的NLPC向量可以得到它们之间的一致性测度.最后,通过多元自适应回归样条函数建立主观MOS分和一致性测度之间的映射关系,可以得到对MOS分的客观预测模型.通过这一模型进行语音质量的客观评价.实验表明,提出算法的性能要好于ITU-T P.563标准中的算法.

Output-based speech quality evaluation based on Non-uniform Linear Prediction Cepstrum and Gaussian Mixture Models

A novel approach for output-based speech quality evaluation is proposed based on Non-uniform Linear Prediction Cepstrum(NLPC) and Gaussian Mixture Models(GMMs).Bark Bilinear Transform(BBT) is employed for spectrum warping that incorporates the non-uniform resolution properties of the human ear.Then,the algorithm computes NLPC coefficients from warped spectrum.GMMs are used to form a reference model of normative behavior by training on features extracted from clean speech signals.A measure of consistency bet...