公开(公告)号：US20070203694A1

公开(公告)日：2007-08-30

申请号：US11364252

申请日：2006-02-28

申请人： Wai-Yip Chan ,  Tiago Falk ,  Mohamed El-Hennawey

发明人： Wai-Yip Chan ,  Tiago Falk ,  Mohamed El-Hennawey

IPC分类号： G10L19/00

CPC分类号： G10L25/69

摘要： A non-intrusive speech quality estimation technique is based on statistical or probability models such as Gaussian Mixture Models (“GMMs”). Perceptual features are extracted from the received speech signal and assessed by an artificial reference model formed using statistical models. The models characterize the statistical behavior of speech features. Consistency measures between the input speech features and the models are calculated to form indicators of speech quality. The consistency values are mapped to a speech quality score using a mapping optimized using machine learning algorithms, such as Multivariate Adaptive Regression Splines (“MARS”). The technique provides competitive or better quality estimates relative to known techniques while having lower computational complexity.

摘要翻译： 非侵入式语音质量估计技术是基于统计或概率模型，如高斯混合模型（“GMM”）。 从接收到的语音信号中提取感知特征，并通过使用统计模型形成的人造参考模型进行评估。 这些模型描述了语音特征的统计行为。 计算输入语音特征和模型之间的一致性度量，以形成语音质量指标。 使用使用机器学习算法优化的映射（例如多变量自适应回归样条（“MARS”））将一致性值映射到语音质量得分。 该技术相对于已知技术提供竞争性或更好的质量估计，同时具有较低的计算复杂度。