A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study.

BACKGROUND

Parkinson disease frequently manifests early vocal impairment, motivating the development of noninvasive and scalable digital screening tools.

OBJECTIVE

This study proposes a multiview spectrogram-based deep learning framework integrating recognition-aware context for Parkinson disease detection from voice recordings.

METHODS

Voice recordings from 203 participants (121 with Parkinson disease and 82 healthy controls) were collected prospectively. Three spectrogram representations (Mel, short-time Fourier transform, and constant-Q transform) were extracted and processed through parallel convolutional neural network branches. A recognition ratio (RR) feature vector derived from automatic speech recognition transcript agreement was optionally fused with spectrogram embeddings. Models were evaluated using strict subject-wise 5-fold cross-validation.

RESULTS

Multiview spectrogram recognition-aware Parkinson detection network achieved a mean test accuracy of 86.9% (SD 25.2%) using 3-view spectrogram fusion, improving to 97.4% (SD 5.7%) when incorporating the RR feature. RR integration reduced the false negative rate by approximately 84.5%, substantially improving sensitivity in screening-oriented settings.

CONCLUSIONS

Combining multiview spectrogram learning with recognition-aware context significantly enhances voice-based Parkinson disease classification under leakage-free evaluation. These findings support the potential of this approach for noninvasive screening in structured recording settings, while further validation in diverse real-world environments is needed.