HPO Based Enhanced Elman Spike Neural Network for Detecting Speech of People with Dysarthria
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Publisher
Allerton Press
Link
https://link.springer.com/content/pdf/10.3103/S1060992X24700097.pdf
Reference24 articles.
1. Gurugubelli Gurugubelli, K., and Vuppala, A.K., Analytic phase features for dysarthric speech detection and intelligibility assessment, Speech Commun., 2020, vol. 121, pp. 1–15.
2. Millet, J. and Zeghidour, N., Learning to detect dysarthria from raw speech, in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019, pp. 5831–5835.
3. Shih, D.H., Liao, C.H., Wu, T.W., Xu, X.Y., and Shih, M.H., Dysarthria speech detection using convolutional neural networks with gated recurrent unit, in Healthcare, MDPI, 2022, vol. 10, no. 10, p. 1956.
4. Ijitona, T.B., Soraghan, J.J., Lowit, A., Di-Caterina, G., and Yue, H., Automatic detection of speech disorder in dysarthria using extended speech feature extraction and neural networks classification, 3rd Internationl Conference on Intelligent Signal Processing, London, United Kingdom, December 2017.
5. Korzekwa, D., Barra-Chicote, R., Kostek, B., Drugman, T., and Lajszczak, M., Interpretable deep learning model for the detection and reconstruction of dysarthric speech. arXiv preprint arXiv:1907.04743, 2019.
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