The Ustc System for Adress-m Challenge
Abstract
This paper describes our submission to the ICASSP 2023 Signal Processing Grand Challenge (SPGC), which focuses on multilingual Alzheimer’s disease (AD) recognition through spontaneous speech. Our approaches include using a variety of acoustic features and silence-related information for AD detection and mini-mental state examination (MMSE) score prediction, and fine-tuning wav2vec2.0 models on speech in various frequency bands for AD detection. Our overall results on the test data outperform the baseline provided by the organizers, achieving 73.9% accuracy in AD detection by fine-tuning our bilingual wav2vec2.0 pre-trained model on the 0-1000Hz frequency band speech, and 4.610 RMSE (r = 0.565) in MMSE prediction through the fusion of eGeMAPS and silence features.
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Mei et al. achieved an accuracy of 87% and an RMSE of 3.727 for AD detection.
“They meticulously fine-tuned the wav2vec2.0 model on speech from assorted frequency bands, culminating in a remarkable accuracy of 87% and an RSME of 3.727.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [83]
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References 4 · Cited by 19
- Authors
- Mei, K., Ding, X., Liu, Y., Guo, Z., Xu, F., Li, X., Naren, T., Yuan, J., & Ling, Z.
- Affiliations
- University of Science and Technology of China (CN)
- IFlyTek (China) (CN)
- University of Science and Technology of China,Hefei,P.R.China
- Venue
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2023-06-04
- Pages
- 1-2
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W4375869155
- Cited by
- 19
- References
- 4
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 6 · 2023: 1
- FWCI
- 3.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- Alzheimer's disease detection, spontaneous speech, multilingual speech recognition, eGeMAPS, acoustic features
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