TIA: A Teaching Intonation Assessment Dataset in Real Teaching Situations

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 th...

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Bibliographic Details
Published inICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1066 - 1070
Main Authors Liu, Shuhua, Zhang, Chunyu, Li, Binshuai, Qin, Niantong, Cheng, Huanting, Zhang, Huayu
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.04.2024
Subjects
Acoustics
Attention Mechanism
Benchmark testing
BiLSTM
Education
Manuals
Recording
Speech processing
Teaching Intonation Assessment Dataset
Teaching Intonation Assessment Model
Wav2vec2.0
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP48485.2024.10447407