WebRTC-QoE: A dataset of QoE assessment of subjective scores, network impairments, and facial & speech features

In the realm of real-time communications, WebRTC-based multimedia applications are increasingly prevalent as these can be smoothly integrated within Web browsing sessions. The browsing experience is then significantly improved with respect to scenarios where browser add-ons and/or plug-ins are used; still, the end user's Quality of Experience (QoE) in WebRTC sessions may be affected by network impairments, such as delays and losses. Due to the variability in user perceptions under different communications scenarios, comprehending and enhancing the resulting service quality is a complex endeavor. To address this, we present a dataset that provides a comprehensive perspective on the conversational quality of a two-party WebRTC-based audiovisual telemeeting service. This dataset was gathered through subjective evaluations involving 20 subjects across 15 different test conditions (TCs). A specialized system was developed to induce controlled network disruptions such as delay, jitter, and packet loss rate, which adversely affected the communication between the parties. This methodology offered an insight into user perceptions under various network impairments. The dataset encompasses a blend of objective and subjective data including ACR (Absolute Category Rating) subjective scores, WebRTC-internals parameters, facial expressions features, and speech features. Consequently, it serves as a substantial contribution to the improvement of WebRTC-based video call systems, offering practical and real-world data that can drive the development of more robust and efficient multimedia communication systems, thereby enhancing the user's experience.