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A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech
Arifa Zahir1, Jaehong Yu2, Jin-Sun Jun3
1Department of Biomedical and Robotics Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea, 82 32-835-8677.
JMIR Medical Informatics
|June 11, 2026
Summary
A new deep learning model using voice analysis can detect Parkinson disease with high accuracy. Integrating speech recognition features significantly improves early detection and reduces false negatives, aiding noninvasive screening.
Area of Science:
- Neurology
- Artificial Intelligence
- Speech Science
Background:
- Parkinson disease often presents with early vocal impairments.
- Developing noninvasive, scalable digital tools for early screening is crucial.
Purpose of the Study:
- To propose a deep learning framework using multiview spectrograms and recognition-aware context for Parkinson disease detection from voice.
- To evaluate the efficacy of integrating automatic speech recognition features with voice spectrograms.
Main Methods:
- Collected voice recordings from 203 participants (121 Parkinson disease, 82 controls).
- Utilized three spectrogram types (Mel, STFT, CQT) processed via parallel CNNs.
- Fused spectrogram embeddings with a recognition ratio (RR) feature vector derived from ASR transcript agreement.
Main Results:
- The multiview spectrogram network achieved 86.9% accuracy.
- Incorporating the RR feature improved accuracy to 97.4%.
- RR integration reduced the false negative rate by 84.5%, enhancing sensitivity.
Conclusions:
- Combining multiview spectrogram learning with recognition-aware context significantly improves voice-based Parkinson disease classification.
- The approach shows potential for noninvasive screening in structured settings.
- Further validation in diverse real-world environments is necessary.
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