Quality of Experience (QoE) measures the overall quality of a service from users’ point of view by considering several system, human, and contextual factors. There exist various objective and subjective methods for QoE prediction. Although the subjective approach is more expensive and challenging than the objective approach, QoE’s level can be more accurately determined by a subjective test. This paper investigates various features affecting QoE by proposing a comprehensive subjective evaluation. First, we show that many unconsidered factors can significantly affect QoE. We have generated voice samples featuring different values for novel factors related to the speaker, signal, and network. Regarding the speaker, we take into account the accent and gender of Persian-speaking people. We conduct an extensive survey by employing a large number of users. Our comprehensive analysis reveals that the users’ identity has a significant influence on QoE. Our experiments show that many previously studied parameters do not affect QoE in the same way for various users with different genders and accents. Finally, we show that QoE can be accurately predicted using Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques if the new identity features are taken into account.

体验质量 (QoE) 通过考虑多个系统、人员和上下文因素，从用户的角度衡量服务的整体质量。QoE 预测存在各种客观和主观的方法。虽然主观方法比客观方法更昂贵和更具挑战性，但通过主观测试可以更准确地确定 QoE 的水平。本文通过提出全面的主观评估来研究影响 QoE 的各种特征。首先，我们表明许多未考虑的因素会显着影响 QoE。我们为与说话者、信号和网络相关的新因素生成了具有不同值的语音样本。关于演讲者，我们考虑了讲波斯语的人的口音和性别。我们通过雇用大量用户进行广泛的调查。我们的综合分析表明，用户身份对 QoE 有显着影响。我们的实验表明，许多先前研究的参数不会以相同的方式影响具有不同性别和口音的各种用户的 QoE。最后，我们表明如果考虑新的身份特征，可以使用人工神经网络 (ANN) 和支持向量回归 (SVR) 技术准确预测 QoE。