Cross-Lingual Alzheimer’s Disease Detection Based on Paralinguistic and Pre-Trained Features
Author:
Affiliation:
1. Tsinghua University,Department of Electronic Engineering,Beijing,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10095522.pdf?arnumber=10095522
Reference10 articles.
1. Unsupervised cross-lingual representation learning for speech recognition;conneau,2020
2. Improving Automatic Speech Recognition Performance for Low-Resource Languages With Self-Supervised Models
3. Robust speech recognition via large-scale weak supervision;radford,2022
4. Alzheimer Disease Recognition Using Speech-Based Embeddings From Pre-Trained Models
5. Automatic detection of alzheimer’s disease using spontaneous speech only;chen;Proc INTERSPEECH,2021
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1. HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer’s Disease Detection From Spontaneous Speech;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
2. A Comparative Survey of Machine Learning Approaches for Alzheimer’s Disease Prediction and Detection;2024 3rd International Conference for Innovation in Technology (INOCON);2024-03-01
3. An Overview of the ADReSS-M Signal Processing Grand Challenge on Multilingual Alzheimer's Dementia Recognition Through Spontaneous Speech;IEEE Open Journal of Signal Processing;2024
4. Transferring Speech-Generic and Depression-Specific Knowledge for Alzheimer’s Disease Detection;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16
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