An Effective Machine Learning (ML) Approach to Quality Assessment of Voice over IP (VoIP) Calls

被引：14

作者：

Cipressi, Elena ^{[1
,2
]}

Merani, Maria Luisa ^{[1
,3
]}

机构：

[1] Research and Development Department, Empirix Inc., Modena

[2] Dipartimento di Ingegneria Enzo Ferrari, University of Modena and Reggio Emilia, Modena

[3] Consorzio Nazionale Interuniversitario per le Telecomunicazioni, Parma

来源：

IEEE Networking Letters | 2020年 / 2卷 / 02期

关键词：

Machine learning (ML); mean opinion score (MOS); quality of experience (QoE); speech quality assessment; voice over IP (VoIP);

D O I：

10.1109/LNET.2020.2984721

中图分类号：

学科分类号：

摘要：

This letter puts forward a supervised ML technique to determine the Quality of Experience (QoE) of VoIP calls. It takes its beginning from an investigation on VQmon, an enhanced E-model version that estimates the quality of IP-based voice calls adopting an objective approach. The current study demonstrates VQmon shortcomings via a comparison between the Mean Opinion Score (MOS) values this technique predicts and the actual average ratings collected from a subjective listening quality campaign. It proposes to deploy Ordinal Logistic Regression (OLR) for speech quality assessment, and results disclose that OLR outperforms popular ML algorithms, in accuracy and confusion matrices. © 2019 IEEE.

引用

页码：90 / 94

页数：4

共 20 条

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