摘要
MOS通常被业界用于评价语音质量,它能够客观公正地反映用户语音业务的感知。通过路测获取数据的方式难度大、成本高,通常采用训练好的监督学习模型预测MOS。但运营商语音数据存在MOS低分数据占比低和时序变化的特性,这种数据特性影响了模型预测的精度和泛化性。在研究现有运营商数据采集系统和机器学习算法的基础上,提出了一种面向5G语音质差MOS评估的自适应算法。首先,基于全参评估的POLQA算法测试设备获取训练数据,保证了训练样本的准确性;其次,通过数据增强的方法,解决了质差样本获取难度大的问题;最后,基于自适应算法选型实现周期性动态地根据数据特征的时序变化选择最佳MOS预测模型,实现5G语音质量规模化、智能化的评估。
MOS(mean opinion score)is usually used to evaluate voice quality in the industry.It can objectively and fairly reflect the user’s voice service perception.It is difficult and costly to obtain data by road test,so a trained supervised learning model is usually used to predict the MOS score.However,the operator voice data has the characteristics of low percentage of MOS low score data and time sequence change,which affects the accuracy and generalization of the model prediction.Based on the study of existing data acquisition systems and machine learning algorithms of operators,an adaptive algorithm for MOS evaluation of 5G speech quality was proposed.Firstly,POLQA algorithm test equipment based on full parameter evaluation obtained training data to ensure the accuracy of training samples.Secondly,by means of data enhancement,the difficulty of acquiring poor quality samples was solved.Finally,based on the adaptive algorithm selection,the optimal MOS prediction model could be selected periodically and dynamically according to the timing changes of data features,so as to achieve large-scale and intelligent evaluation of 5G voice quality.
作者
赵宇翔
纪雅欣
余立
周天一
周航
ZHAO Yuxiang;JI Yaxin;YU Li;ZHOU Tianyi;ZHOU Hang(China Mobile(Zhejiang)Research&Innovation Institute Co.,Ltd.,Hangzhou 310030,China)
出处
《电信科学》
2023年第11期153-163,共11页
Telecommunications Science
