Soft-Weighted CrossEntropy Loss for Continous Alzheimer's Disease Detection (2402.11931v1)
Abstract: 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.
- Alzheimer’s Disease Decoded: The History, Present, and Future of Alzheimer’s Disease and Dementia, Alzheimer’s Disease Decoded: The History, Present, and Future of Alzheimer’s Disease and Dementia, 2021.
- “Alzheimer’s disease detection from spontaneous speech through combining linguistic complexity and (dis) fluency features with pretrained language models,” arXiv preprint arXiv:2106.08689, 2021.
- “What to remember: Self-adaptive continual learning for audio deepfake detection,” arXiv preprint arXiv:2312.09651, 2023.
- “Do you remember? Overcoming catastrophic forgetting for fake audio detection,” in Proceedings of the 40th International Conference on Machine Learning, Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, Eds. 23–29 Jul 2023, vol. 202 of Proceedings of Machine Learning Research, pp. 41819–41831, PMLR.
- “Multimodal representation learning by alternating unimodal adaptation,” 2023.
- “Detecting cognitive decline using speech only: The adresso challenge,” arXiv preprint arXiv:2104.09356, 2021.
- “The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing,” IEEE transactions on affective computing, vol. 7, no. 2, pp. 190–202, 2015.
- “An automatic assessment system for alzheimer’s disease based on speech using feature sequence generator and recurrent neural network,” Scientific reports, vol. 9, no. 1, pp. 1–10, 2019.
- “Unsupervised cross-lingual representation learning for speech recognition,” arXiv preprint arXiv:2006.13979, 2020.
- “wav2vec 2.0: A framework for self-supervised learning of speech representations,” arXiv preprint arXiv:2006.11477, 2020.
- “Self-training and pre-training are complementary for speech recognition,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 3030–3034.
- “A comparative study of feature extraction methods for the diagnosis of alzheimer’s disease using the adni database,” Neurocomputing, vol. 75, no. 1, pp. 64–71, 2012.
- “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
- “A comparative analysis of speech signal processing algorithms for parkinson’s disease classification and the use of the tunable q-factor wavelet transform,” Applied Soft Computing, vol. 74, pp. 255–263, 2019.