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

This letter puts forward a supervised ML tech2 nique 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.