HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer’s Disease Detection From Spontaneous Speech-Reference-Cited by-同舟云学术

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HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer’s Disease Detection From Spontaneous Speech

Published:2024-04-14 Issue: Volume: Page:11246-11250
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Dong Zhongren¹,Zhang Zixing¹,Xu Weixiang¹,Han Jing²,Ou Jianjun³,Schuller Björn W.⁴

Affiliation:

1. Hunan University,College of Computer Science and Electronic Engineering,China

2. University of Cambridge,Department of Computer Science and Technology,UK

3. Central South University,The Second Xiangya Hospital,Department of Psychiatry,China

4. Imperial College,GLAM,Department of Computing,London,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446795.pdf?arnumber=10446795

Reference26 articles.

1. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission

2. Neuropsychological Predictors of Conversion from Mild Cognitive Impairment to Alzheimer's Disease

3. Multilingual Alzheimer’s Dementia Recognition through Spontaneous Speech: A Signal Processing Grand Challenge

4. Comparing Acoustic-Based Approaches for Alzheimer’s Disease Detection

5. Alzheimer’s Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs

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