Deep Learning Approach for Analysis of Audio for the Diagnosis of Alzheimer
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-69197-3_1
Reference19 articles.
1. Vats, N.A., Barche, P., Mirishkar, G.S., Vuppala, A.K.: Exploring high spectro-temporal resolution for alzheimer’s dementia detection. In: 2022 IEEE International Conference on Signal Processing and Communications (SPCOM), Bangalore, India, pp. 1–5 (2022). https://doi.org/10.1109/SPCOM55316.2022.9840847
2. Liu, Z., Proctor, L., Collier, P.N., Zhao, X.: Automatic diagnosis and prediction of cognitive decline associated with alzheimer’s dementia through spontaneous speech. In: 2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Terengganu, Malaysia, pp. 39–43 (2021). https://doi.org/10.1109/ICSIPA52582.2021.9576784
3. Valsaraj, A., Madala, I., Garg, N., Baths, V.: Alzheimer’s dementia detection using acoustic & linguistic features and pre-trained BERT. In: 2021 8th International Conference on Soft Computing & Machine Intelligence (ISCMI), Cario, Egypt, pp. 171–175 (2021). https://doi.org/10.1109/ISCMI53840.2021.9654804
4. Luz, S., Haider, F., de la Fuente, S., Fromm, D., MacWhinney, B.: Alzheimer's dementia recognition through spontaneous speech: the ADReSS challenge. In: Proceedings of INTERSPEECH (2020). arXiv preprint arXiv:2004.06833. https://doi.org/10.48550/arXiv.2004.06833
5. de la Fuente Garcia, S., Ritchie, C., Luz, S.: Artificial intelligence, speech, and language processing approaches to monitoring alzheimer's disease: a systematic review. J. Alzheimer's Dis. 1–27 (2020). https://doi.org/10.3233/JAD-200888
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