以测度作为神经网络输入的客观音质评价研究  

Object speech quality assessment by neural network with distance measure as inputs

作　　者：黄丽[1] 周杰[1] 马洪[1] 张知易 吴江滨 

机构地区：[1]四川大学数学学院,成都610064 [2]中电科技集团第30研究所,610041

出　　处：《四川大学学报（自然科学版）》2007年第6期1210-1214,共5页Journal of Sichuan University(Natural Science Edition)

基　　金：国家自然科学基金(10571127);973项目(2002cb312206)

摘　　要：利用径向基函数网络(RBFN)进行语音质量客观评价,以避免在回归分析中选取具体函数的困难.选取3种距离测度而非语音文件本身作为神经网络的输入,使得网络输入维数大大减小,网络结构大大简化.且对径向基函数网络结构作了修正,使其更便于作音质评价.作者在网络参数和结构学习中采用平滑后的训练集,有效减少了随机因素对客观评测结果的影响,也大大减少了网络结构的复杂性.主客观评价结果的相关性实验中,相关系数达0.96以上,这表明了该方法的可靠性.In this paper radial-basis function network （RBFN） is used for objective speech quality assessment in order to avoid the difficulty of choose regression function, Three kind of distance measure rather than the speech itself are chosen as the inputs of the neural network, so the dimensions of neural network＇s input is de- creased greatly. Thus the structure of neural network is simplified greatly, The structure of RBFN is also modified so as to convenient for speech quality assessment. Using smoothed training set for learning of parameters and structure of RBFN, the affection of random factors on the result of objective assessment can be reduced effectively, and the neural network＇ s structure can be simplified. The experimental results show that the structure of obtained RBFN is very simple, and the correlation coefficient between the subject seores and object MOS estimate is above 0.96. This shows that the method is reliable.

关 键 词：语音质量客观评价 测度 径向基函数网络 

分 类 号：TN912.3[电子电信—通信与信息系统]