A Novel Recurrent Generalized Congruence Neural Network Measure for Objective Speech Quality Evaluation

被引：0

作者：

Yan, Tian-yun ^{[1
]}

机构：

[1] Chengdu Univ Informat Technol, Coll Comp Sci & Technol, Chengdu 610225, Peoples R China

来源：

MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8 | 2012年 / 433-440卷

关键词：

MOS; recurrent neural network; RBF; speech quality;

D O I：

10.4028/www.scientific.net/AMR.433-440.2282

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A new system model for objective speech quality evaluation based on the improved recurrent generalized congruence neural network (RGCNN/OSQE) is proposed. The performance of the RGCNN model is compared with the most commonly used RBFNN (radial basis function neural network) model in objective speech quality evaluation. Comparison results show that the RGCNN model has higher correlation coefficient, less deviation, and saves about half training time, i.e., the RGCNN model has obvious advantages over the RBFNN model. Therefore, the novel RGCNN model for objective speech quality evaluation is feasible and effective.

引用

页码：2282 / 2287

页数：6