AI-based stuttering automatic classification method: Using a convolutional neural network*

Author:

Park Jin,Lee Chang Gyun

Funder

National Research Foundation of Korea

Publisher

The Korean Society of Speech Sciences

Subject

Materials Science (miscellaneous)

Reference33 articles.

1. Altinkaya, M., & Smeulders, A. W. M. (2020, October). A dynamic, self supervised, large scale audiovisual dataset for stuttered speech. Proceedings of the 1st International Workshop on Multimodal Conversational AI (pp. 9-13). Seattle, WA. 10.1145/3423325.3423733

2. Barrett, L., Hu, J., & Howell, P. (2022). Systematic review of machine learning approaches for detecting developmental stuttering. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 1160-1172. 10.1109/TASLP.2022.3155295

3. Bayerl, S. P., von Gudenberg, A. W., Hönig, F., Nöth, E., & Riedhammer, K. (2022, June). KSoF: The Kassel state of fluency dataset – A therapy centered dataset of stuttering. Proceedings of the 13th Conference on Language Resources and Evaluation (pp. 1780-1787). Marseille, France.

4. Bhushan, P. S., Vani, H. Y., Shivkumar, D. K., & Sreeraksha, M. R. (2021). Stuttered Speech Recognition using Convolutional Neural Networks, International Journal of Engineering Research & Technology, 9(12), 250-254.

5. Das, A., Mock, J. Irani, F., Huang, Y., Najafirad, P., & Golob, E. (2022). Multimodal explainable AI predicts upcoming speech behavior in adults who stutter. Frontiers in Neuroscience, 16:912798. 10.3389/fnins.2022.912798 35979337 PMC9376608

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