A novel fusion architecture for detecting Parkinson’s Disease using semi-supervised speech embeddings
Published in npj Parkinson’s Disease, 2025
This work introduces a speech-based Parkinson’s disease screening framework using English pangram utterances from $\mathbf{1,306}$ participants. By fusing semi-supervised Wav2Vec2 and ImageBind speech embeddings into a multimodal classifier, the model achieved $\mathbf{\sim89\%}$ AUROC and $\mathbf{\sim86\%}$ accuracy, with strong external robustness.
Recommended citation: Adnan, T., Abdelkader, A., Liu, Z., Hossain, E., Park, S., Islam, M.S. and Hoque, E., 2025. A novel fusion architecture for detecting Parkinson’s Disease using semi-supervised speech embeddings. npj Parkinson's Disease, 11(1), p.176.
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