Metaverse provides users with a novel experience through immersive multimedia technologies. Along with the rapid user growth, numerous events bursting in the metaverse necessitate an announcer to help catch and monitor ongoing events. However, systems on the market primarily serve for esports competitions and rely on human directors, making it challenging to provide 24-hour delivery in the metaverse persistent world. To fill the blank, we proposed a three-stage architecture for metaverse announcers, which is designed to identify events, position cameras, and blend between shots. Based on the architecture, we introduced a Metaverse Announcer User Experience (MAUE) model to identify the factors affecting the users' Quality of Experience (QoE) from a human-centered perspective. In addition, we implemented \textit{MetaCast}, a practical self-driven metaverse announcer in a university campus metaverse prototype, to conduct user studies for MAUE model. The experimental results have effectively achieved satisfactory announcer settings that align with the preferences of most users, encompassing parameters such as video transition rate, repetition rate, importance threshold value, and image composition.

翻译：元宇宙通过沉浸式多媒体技术为用户提供新颖的体验。随着用户快速增长，元宇宙中爆发的大量事件需要播报系统帮助捕捉和监控进行中的事件。然而，市场上的系统主要服务于电子竞技比赛且依赖人工导演，难以在元宇宙持久化世界中提供全天候服务。为填补这一空白，我们提出了一种面向元宇宙播报系统的三阶段架构，该架构设计用于识别事件、定位摄像机以及实现镜头间的混合切换。基于该架构，我们引入了元宇宙播报用户体验（MAUE）模型，从以人为中心的角度识别影响用户体验质量（QoE）的因素。此外，我们在一个大学校园元宇宙原型中实现了实用的自驱动元宇宙播报系统\textit{MetaCast}，用于开展MAUE模型的用户研究。实验结果有效获得了符合大多数用户偏好的播报系统设置参数，包括视频转换率、重复率、重要性阈值以及图像构图等。