The quality of vocal delivery is one of the key indicators for evaluating teacher enthusiasm, which has been widely accepted to be connected to the overall course qualities. However, existing evaluation for vocal delivery is mainly conducted with manual ratings, which faces two core challenges: subjectivity and time-consuming. In this paper, we present a novel machine learning approach that utilizes pairwise comparisons and a multimodal orthogonal fusing algorithm to generate large-scale objective evaluation results of the teacher vocal delivery in terms of fluency and passion. We collect two datasets from real-world education scenarios and the experiment results demonstrate the effectiveness of our algorithm. To encourage reproducible results, we make our code public available at \url{https://github.com/tal-ai/ML4VocalDelivery.git}.

中文翻译：

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用于教师声乐交付评估的多模态机器学习框架

发声质量是评价教师积极性的关键指标之一，已被广泛接受与整体课程质量相关。然而，现有的语音传递评估主要是通过人工评分进行的，这面临着两个核心挑战：主观性和耗时。在本文中，我们提出了一种新颖的机器学习方法，该方法利用成对比较和多模态正交融合算法，在流畅性和热情方面生成大规模的教师声乐传递的客观评估结果。我们从现实世界的教育场景中收集了两个数据集，实验结果证明了我们算法的有效性。为了鼓励可重现的结果，我们在 \url{https://github.com/tal-ai/ML4VocalDelivery.git} 上公开了我们的代码。