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Computer Science > Sound

arXiv:2312.08732 (cs)
[Submitted on 14 Dec 2023]

Title:TIA: A Teaching Intonation Assessment Dataset in Real Teaching Situations

Authors:Shuhua Liu, Chunyu Zhang, Binshuai Li, Niantong Qin, Huanting Cheng, Huayu Zhang
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Abstract: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.
Comments: 4 pages, 3 figures, 4 tables, accepted by 2024 International Conference on Acoustics, Speech, and Signal Processing (ICASSP2024)
Subjects: Sound (cs.SD); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2312.08732 [cs.SD]
  (or arXiv:2312.08732v1 [cs.SD] for this version)
  https://doi.org/10.48550/arXiv.2312.08732

Submission history

From: Chunyu Zhang [view email]
[v1] Thu, 14 Dec 2023 08:20:48 UTC (3,727 KB)
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