Speech-Based Parkinson’s Disease Screening: A Deep Learning Approach Using Acoustic Biomarkers and Invertible Neural Networks

Prisca O. Olawoye, Emmanuel O. Asani, Marion O. Adebiyi
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:355-367, 2026.

Abstract

We propose a deep learning architecture combining deep residual encoders and squeeze-and-excitation attention mechanisms with invertible normalising flows for speech-based Parkinson’s disease (PD) screening. On the Telemonitoring dataset, the model achieves AUC 0.979, 100% specificity, 72.4% sensitivity, and 79.5% accuracy. On the MSR dataset, it achieves 71.1% accuracy, AUC 0.806, with symmetric sensitivity (71.0%) and specificity (71.2%). Invertible normalising flows enable exact density estimation and principled uncertainty quantification, supporting telemedicine applications for smartphone-based remote screening of Parkinson’s disease.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-olawoye26a, title = {Speech-Based {Parkinson’s} Disease Screening: A Deep Learning Approach Using Acoustic Biomarkers and Invertible Neural Networks}, author = {Olawoye, Prisca O. and Asani, Emmanuel O. and Adebiyi, Marion O.}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {355--367}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/olawoye26a/olawoye26a.pdf}, url = {https://proceedings.mlr.press/v319/olawoye26a.html}, abstract = {We propose a deep learning architecture combining deep residual encoders and squeeze-and-excitation attention mechanisms with invertible normalising flows for speech-based Parkinson’s disease (PD) screening. On the Telemonitoring dataset, the model achieves AUC 0.979, 100% specificity, 72.4% sensitivity, and 79.5% accuracy. On the MSR dataset, it achieves 71.1% accuracy, AUC 0.806, with symmetric sensitivity (71.0%) and specificity (71.2%). Invertible normalising flows enable exact density estimation and principled uncertainty quantification, supporting telemedicine applications for smartphone-based remote screening of Parkinson’s disease.} }
Endnote
%0 Conference Paper %T Speech-Based Parkinson’s Disease Screening: A Deep Learning Approach Using Acoustic Biomarkers and Invertible Neural Networks %A Prisca O. Olawoye %A Emmanuel O. Asani %A Marion O. Adebiyi %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-olawoye26a %I PMLR %P 355--367 %U https://proceedings.mlr.press/v319/olawoye26a.html %V 319 %X We propose a deep learning architecture combining deep residual encoders and squeeze-and-excitation attention mechanisms with invertible normalising flows for speech-based Parkinson’s disease (PD) screening. On the Telemonitoring dataset, the model achieves AUC 0.979, 100% specificity, 72.4% sensitivity, and 79.5% accuracy. On the MSR dataset, it achieves 71.1% accuracy, AUC 0.806, with symmetric sensitivity (71.0%) and specificity (71.2%). Invertible normalising flows enable exact density estimation and principled uncertainty quantification, supporting telemedicine applications for smartphone-based remote screening of Parkinson’s disease.
APA
Olawoye, P.O., Asani, E.O. & Adebiyi, M.O.. (2026). Speech-Based Parkinson’s Disease Screening: A Deep Learning Approach Using Acoustic Biomarkers and Invertible Neural Networks. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:355-367 Available from https://proceedings.mlr.press/v319/olawoye26a.html.

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