Multi-Speaker Identification with IoT Badges for Collaborative Learning Analysis
Author:
Affiliation:
1. Graduate School of Information Science and Technology, Osaka University
2. Graduate School of Integrated Science and Technology, Shizuoka University
Publisher
Information Processing Society of Japan
Subject
General Computer Science
Link
https://www.jstage.jst.go.jp/article/ipsjjip/31/0/31_375/_pdf
Reference56 articles.
1. [1] Ajgou, R., Sbaa, S., Ghendir, S., Chamsa, A. and Taleb-Ahmed, A.: Robust Remote Speaker Recognition System Based on AR-MFCC Features and Efficient Speech Activity Detection Algorithm, International Symposium on Wireless Communications Systems, pp.722–727 (2014).
2. [2] Backer, L. D., Keer, H. V., Smedt, F. D., Merchie, E. and Valcke, M.: Identifying regulation profiles during computer-supported collaborative learning and examining their relation with students' performance, motivation, and self-efficacy for learning, Computers & Education, Vol.179, p.104421 (2022).
3. [3] Belfield,W. and Mikkilineni, R.: Speaker Verification Based on a Vector Quantization Approach that Incorporates Speaker Cohort Models and a Linear Discriminator, IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, pp.4525–4529 (1997).
4. [4] Biagetti, G., Crippa, P., Curzi, A., Orcioni, S. and Turchetti, C.: Speaker Identification with Short Sequences of Speech Frames, International Conference on Pattern Recognition Applications and Methods, pp.178–185 (2015).
5. [5] Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S. and Turchetti, C.: Speaker Identification in Noisy Conditions Using Short Sequences of Speech Frames, Smart Innovation, Systems and Technologies, pp.43–52 (2018).
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