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.

翻译：语调是影响教学语言艺术的重要因素之一，因此通过人工智能技术评估教师语调成为亟待解决的问题。然而，语调评估数据集的缺失阻碍了该领域的发展。为此，本文首次在真实教学场景中构建了教学语调评估（TIA）数据集。该数据集涵盖9个学科、396名教师，共计11,444个时长为15秒的语音样本。为验证数据集的有效性，本文提出基于语音低层与深层特征的教学语调评估模型（TIAM）。实验结果表明，基于本文所构建数据集的TIAM模型与人工评估结果基本一致，且性能优于基线模型，验证了该评估模型的有效性。