  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}.

Sound;Computation and Language;Audio and Speech Processing