Design of a dual-route acoustic-feature and EfficientNet-B0 spectrogram-based voice-screening device for Parkinson's disease assessment.

Research overview

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ObjectivesParkinson's disease (PD) can affect vocal production before severe motor symptoms become apparent. This study presents a device-oriented dual-route voice-screening framework that combines interpretable acoustic descriptors with spectrogram-based transfer learning for PD assessment support.MethodsPublicly available sustained-vowel /a/ recordings from the Parkinson Speech Dataset with Multiple Types of Sound Recordings were used for algorithmic evaluation. The proposed handheld device, dashboard, and connectivity workflow were treated as a conceptual deployment scenario. The acoustic route generated an integrated acoustic-feature vector and grouped representation-level summaries of selected descriptor families. The spectrogram route generated RGB color-mapped and grayscale time-frequency representations, which were processed using an EfficientNet-B0 transfer-learning convolutional neural network.ResultsAmong conventional acoustic-feature classifiers, the Ensemble Boosting Classifier achieved the highest AUC point estimate for the integrated acoustic-feature route. Grouped representation-level evidence showed weak-to-moderate discrimination for acoustic descriptor families. RGB and grayscale spectrograms processed through the EfficientNet-B0 route produced reported AUC point estimates of 0.93 and 0.89, respectively, within the available public-dataset evaluation.ConclusionsThe proposed framework combines interpretable acoustic descriptors and spectrogram-based transfer learning within a device-oriented screening-support workflow. Integrated acoustic features support interpretable reporting, while spectrogram-based transfer learning provides higher representation-level discrimination. Future validation with physical prototypes, standardized acquisition protocols, and independent clinical cohorts is required before clinical deployment.

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