Effective leadership bears strong relationship to attributes of emotion contagion, positive mood, and social intelligence. In fact, leadership quality has been shown to be manifested in the exhibited communicative behaviors, especially in settings of public speaking. While studies on the theories of leadership has received much attention, little has progressed in terms of the computational development in its measurements. In this work, we present a behavioral signal processing (BSP) research to assess the qualities of oral presentations in the domain of education, in specific, we propose a multimodal framework toward automating the scoring process of pre-service school principals’ oral presentations given at the yearly certification program. We utilize a dense unit-level audio-video feature extraction approach with session-level behavior profile representation techniques based on bag-of-word and Fisher-vector encoding. Furthermore, we design a scoring framework, inspired by the psychological evidences of human's decision-making mechanism, to use confidence measures outputted from support vector machine classifier trained on the distinctive set of data samples as the regressed scores. Our proposed approach achieves an absolute improvement of 0.049 (9.8 percent relative) on average over support vector regression. We further demonstrate that the framework is reliable and consistent compared to human experts.

中文翻译：

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在使用音视频低级行为档案的校长认证计划期间实现自动口头演讲评分

有效的领导力与情绪传染、积极情绪和社交智力等属性密切相关。事实上，领导素质已被证明表现在表现出的交际行为上，尤其是在公开演讲的场合。虽然对领导理论的研究受到了很多关注，但在其测量的计算发展方面进展甚微。在这项工作中，我们提出了一项行为信号处理 (BSP) 研究，以评估教育领域口头报告的质量，具体而言，我们提出了一个多模式框架，以自动化给定的职前校长口头报告的评分过程在年度认证计划中。我们利用基于词袋和Fisher矢量编码的会话级行为配置文件表示技术的密集单元级音频-视频特征提取方法。此外，我们设计了一个评分框架，受到人类决策机制的心理证据的启发，使用从支持向量机分类器输出的置信度度量，该分类器在独特的数据样本集上训练作为回归分数。我们提出的方法比支持向量回归平均实现了 0.049（相对 9.8%）的绝对改进。我们进一步证明，与人类专家相比，该框架是可靠且一致的。s 决策机制，使用从支持向量机分类器输出的置信度度量，在独特的数据样本集上训练作为回归分数。我们提出的方法比支持向量回归平均实现了 0.049（相对 9.8%）的绝对改进。我们进一步证明，与人类专家相比，该框架是可靠且一致的。s 决策机制，使用从支持向量机分类器输出的置信度度量，在独特的数据样本集上训练作为回归分数。我们提出的方法比支持向量回归平均实现了 0.049（相对 9.8%）的绝对改进。我们进一步证明，与人类专家相比，该框架是可靠且一致的。