Speaker Diarization in the Classroom: How Much Does Each Student Speak in Group Discussions?
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| Title: | Speaker Diarization in the Classroom: How Much Does Each Student Speak in Group Discussions? |
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| Language: | English |
| Authors: | Jiani Wang, Shiran Dudy, Xinlu He, Zhiyong Wang, Rosy Southwell, Jacob Whitehill |
| Source: | International Educational Data Mining Society. 2024. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Peer Reviewed: | Y |
| Page Count: | 8 |
| Publication Date: | 2024 |
| Sponsoring Agency: | National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL) National Science Foundation (NSF) |
| Contract Number: | 2019805 2046505 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Junior High Schools Middle Schools Secondary Education High Schools |
| Descriptors: | Group Discussion, Interpersonal Communication, Self Expression, Speech Communication, Automation, Identification, Audio Equipment, Computer Software, Group Behavior, Middle School Students, High School Students, Group Dynamics |
| Abstract: | One important dimension of classroom group dynamics & collaboration is how much each person contributes to the discussion. With the goal of measuring how much each student speaks, we investigate how automatic speaker diarization can be built to handle real-world classroom group discussions. We examine key design considerations such as the level of granularity of speaker assignment, speech enhancement techniques, voice activity detection, and embedding assignment method, so as to find an effective configuration. The best speaker diarization that we found was based on the ECAPA-TDNN speaker embedding model and used Whisper automatic speech recognition to find speech segments. Diarization error rates (DER) on challenging noisy spontaneous classroom data were around 34%, and the correlations of estimated vs. human annotations of how much each student spoke reached 0.62. The presented diarization system has potential to benefit educational research and also to give teachers and students useful feedback to understand their group dynamics. [For the complete proceedings, see ED675485.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED675561 |
| Database: | ERIC |
| Abstract: | One important dimension of classroom group dynamics & collaboration is how much each person contributes to the discussion. With the goal of measuring how much each student speaks, we investigate how automatic speaker diarization can be built to handle real-world classroom group discussions. We examine key design considerations such as the level of granularity of speaker assignment, speech enhancement techniques, voice activity detection, and embedding assignment method, so as to find an effective configuration. The best speaker diarization that we found was based on the ECAPA-TDNN speaker embedding model and used Whisper automatic speech recognition to find speech segments. Diarization error rates (DER) on challenging noisy spontaneous classroom data were around 34%, and the correlations of estimated vs. human annotations of how much each student spoke reached 0.62. The presented diarization system has potential to benefit educational research and also to give teachers and students useful feedback to understand their group dynamics. [For the complete proceedings, see ED675485.] |
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