论文标题
事实说明书：自动自我报告的人格识别曲目
Fact sheet: Automatic Self-Reported Personality Recognition Track
论文作者
论文摘要
我们提出了一个知情的基线，以帮助解散此类案例研究中影响的各种影响因素。为此，我们分析了给定的元数据与自我分配的人格特质分数之间的相关性，并仅基于此信息开发了模型。此外，我们将该知情基线的性能与基于最先进的视觉，语言和音频功能的模型进行了比较。对于本数据集，与简单的音频，基于语言或基于视觉功能的系统相比，仅根据简单的元数据功能（年龄，性别和会话数）进行了训练的模型。
We propose an informed baseline to help disentangle the various contextual factors of influence in this type of case studies. For this purpose, we analysed the correlation between the given metadata and the self-assigned personality trait scores and developed a model based solely on this information. Further, we compared the performance of this informed baseline with models based on state-of-the-art visual, linguistic and audio features. For the present dataset, a model trained solely on simple metadata features (age, gender and number of sessions) proved to have superior or similar performance when compared with simple audio, linguistic or visual features-based systems.
