Détail du document
Identifiant
oai:arXiv.org:2402.11931
Sujet
Computer Science - Sound Electrical Engineering and Systems... Quantitative Biology - Neurons and...Auteur
Zhang, Xiaohui Fu, Wenjie Liang, ManguiCatégorie
Computer Science
Année
2024
Date de référencement
21/02/2024
Mots clés
loss alzheimer diseaseRésumé
Alzheimer's disease is a common cognitive disorder in the elderly.
Early and accurate diagnosis of Alzheimer's disease (AD) has a major impact on the progress of research on dementia.
At present, researchers have used machine learning methods to detect Alzheimer's disease from the speech of participants.
However, the recognition accuracy of current methods is unsatisfactory, and most of them focus on using low-dimensional handcrafted features to extract relevant information from audios.
This paper proposes an Alzheimer's disease detection system based on the pre-trained framework Wav2vec 2.0 (Wav2vec2).
In addition, by replacing the loss function with the Soft-Weighted CrossEntropy loss function, we achieved 85.45\% recognition accuracy on the same test dataset.
Zhang, Xiaohui,Fu, Wenjie,Liang, Mangui, 2024, Soft-Weighted CrossEntropy Loss for Continous Alzheimer's Disease Detection
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