Automatic selection of spoken language biomarkers for dementia detection

Author:

Ke XiaoquanORCID,Mak Man WaiORCID,Meng Helen M.

Funder

Research Grants Council, University Grants Committee

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference48 articles.

1. Connected speech as a marker of disease progression in autopsy-proven Alzheimer’s disease;Ahmed;Brain,2013

2. Alhanai, T., Au, R., & Glass, J. (2017). Spoken language biomarkers for detecting cognitive impairment. In Proc. IEEE autom. speech recognit. underst. workshop (pp. 409–416).

3. Ambroise, C., & McLachlan, G. J. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. In Proc. natl. acad. sci. USA (pp. 6562–6566).

4. Ammar, R. B., & Ayed, Y. B. (2018). Speech Processing for Early Alzheimer Disease Diagnosis: Machine Learning Based Approach. In Proc. IEEE/ACS int. conf. comput. syst. appl. (pp. 1–8).

5. Baevski, A., Zhou, Y., Mohamed, A., & Auli, M. (2020). Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. In Proc. adv. neural inf. proces. syst. (pp. 12449–12460).

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