Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Feb 24, 2026
Date Accepted: May 10, 2026
A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study
ABSTRACT
Background:
Parkinson’s disease (PD) frequently manifests early vocal impairment, motivating the development of non-invasive and scalable digital screening tools.
Objective:
This study proposes MSR-PDNet, a multiview spectrogram- based deep learning framework integrating recognition-aware context for PD detection from voice recordings.
Methods:
Voice recordings from 203 participants (121 PD, 82 healthy controls) were collected prospectively. Three spectrogram representations(Mel, STFT, and CQT) were extracted and processed via parallel convo- lutional neural network branches. A recognition ratio (RR) feature vector derived from automatic speech recognition transcript agreement was option- ally fused with spectrogram embeddings. Models were evaluated using strict subject-wise 5-fold cross-validation.
Results:
MSR-PDNet achieved 86.9% mean test accuracy using three- view spectrogram fusion, improving to 97.4% when incorporating RR. RR integration reduced the false negative rate by approximately 84.5%, substan- tially improving sensitivity in screening-oriented settings.
Conclusions:
Combining multiview spectrogram learning with recognition- aware context significantly enhances voice-based PD classification under leakage- free evaluation. The proposed framework supports deployment-oriented, non-invasive PD screening systems.
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Copyright
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