In most public speaking datasets, judgements are given after watching the entire performance, or on thin slices randomly selected from the presentations, without focusing on the temporal location of these slices. This does not allow to investigate how people’s judgements develop over time during presentations. This contrasts with primacy and recency theories, which suggest that some moments of the speech could be more salient than others and contribute disproportionately to the perception of the speaker’s performance. To provide novel insights on this phenomenon, we present the 3MT_French dataset. It contains a set of public speaking annotations collected on a crowd-sourcing platform through a novel annotation scheme and protocol. Global evaluation, persuasiveness, perceived self-confidence of the speaker and audience engagement were annotated on different time windows (i.e., the beginning, middle or end of the presentation, or the full video). This new resource will be useful to researchers working on public speaking assessment and training. It will allow to fine-tune the analysis of presentations under a novel perspective relying on socio-cognitive theories rarely studied before in this context, such as first impressions and primacy and recency theories. An exploratory correlation analysis on the annotations provided in the dataset suggests that the early moments of a presentation have a stronger impact on the judgements.

在大多数公开演讲数据集中，判断是在观看整个表演后做出的，或者是在从演示中随机选择的薄片上做出的，而不关注这些切片的时间位置。这无法调查人们的判断在演示过程中如何随着时间的推移而发展。这与首要性理论和新近性理论形成鲜明对比，后者认为演讲中的某些时刻可能比其他时刻更加突出，并且对演讲者表演的感知有不成比例的贡献。为了提供对这一现象的新颖见解，我们提出了 3MT_French 数据集。它包含通过新颖的注释方案和协议在众包平台上收集的一组公开演讲注释。在不同的时间窗口（即演示的开始、中间或结束或完整视频）对总体评价、说服力、演讲者的自信心和观众参与度进行了注释。这一新资源将对从事公开演讲评估和培训的研究人员有用。它将允许在依赖于在此背景下很少研究的社会认知理论（例如第一印象、首要性和近因性理论）的新颖视角下对演示文稿的分析进行微调。对数据集中提供的注释进行的探索性相关分析表明，演示的早期时刻对判断有更大的影响。