  Intonation is one of the important factors affecting the teaching language
arts, so it is an urgent problem to be addressed by evaluating the teachers'
intonation through artificial intelligence technology. However, the lack of an
intonation assessment dataset has hindered the development of the field. To
this end, this paper constructs a Teaching Intonation Assessment (TIA) dataset
for the first time in real teaching situations. This dataset covers 9
disciplines, 396 teachers, total of 11,444 utterance samples with a length of
15 seconds. In order to test the validity of the dataset, this paper proposes a
teaching intonation assessment model (TIAM) based on low-level and deep-level
features of speech. The experimental results show that TIAM based on the
dataset constructed in this paper is basically consistent with the results of
manual evaluation, and the results are better than the baseline models, which
proves the effectiveness of the evaluation model.
